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dhrumilparikh

FIX: Removed duplicate blog entries and made build script idempotent

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	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Fleet Runner — Fleet AI Oversight</title>
	<link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700&family=DM+Mono:wght@400;500&display=swap" rel="stylesheet">
	<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
	<style>
	, ::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }

	:root {
	--sidebar-bg: #ffffff;
	--sidebar-text: #64748b;
	--sidebar-active: #2563eb;
	--sidebar-accent: #2563eb;
	--canvas: #f8fafc;
	--surface: #ffffff;
	--border: #e2e8f0;
	--border-soft: #f1f5f9;
	--text-primary: #0f172a;
	--text-secondary: #64748b;
	--text-mono: #475569;
	--indigo: #2563eb;
	--indigo-soft: #eff6ff;
	--indigo-border: #bfdbfe;
	--emerald: #10b981;
	--emerald-soft: #ecfdf5;
	--amber: #f59e0b;
	--amber-soft: #fffbeb;
	--rose: #ef4444;
	--rose-soft: #fef2f2;
	--gray-100: #f1f5f9;
	--gray-200: #e2e8f0;
	--gray-300: #cbd5e1;
	--gray-400: #94a3b8;
	--radius: 12px;
	--radius-sm: 8px;
	--shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
	--shadow-md: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
	}

	body { font-family: 'Plus Jakarta Sans', sans-serif; background: var(--canvas); color: var(--text-primary); font-size: 13px; line-height: 1.5; min-height: 100vh; }

	/* ── Sidebar ── */
	.app-shell { display: flex; min-height: 100vh; }
	.sidebar { width: 220px; flex-shrink: 0; background: var(--sidebar-bg); display: flex; flex-direction: column; position: sticky; top: 0; height: 100vh; overflow-y: auto; border-right: 1px solid var(--border); }
	.sidebar-logo { padding: 22px 20px 18px; display: flex; align-items: center; gap: 10px; }
	.logo-mark { width: 32px; height: 32px; background: var(--sidebar-accent); border-radius: 8px; display: flex; align-items: center; justify-content: center; flex-shrink: 0; font-size: 16px; color: #fff; }
	.logo-text { font-size: 14px; font-weight: 700; color: var(--text-primary); line-height: 1.2; }
	.logo-text span { display: block; font-size: 10px; color: var(--text-secondary); font-weight: 500; margin-top: 2px; }
	.nav-section { padding: 24px 20px 8px; font-size: 11px; font-weight: 700; color: var(--gray-400); text-transform: uppercase; letter-spacing: 0.05em; }
	.nav-item { display: flex; align-items: center; gap: 10px; padding: 10px 16px; margin: 2px 12px; border-radius: 8px; font-size: 13px; font-weight: 500; color: var(--sidebar-text); text-decoration: none; transition: all 0.2s; cursor: pointer; }
	.nav-item:hover { background: var(--gray-100); color: var(--text-primary); }
	.nav-item.active { background: var(--indigo-soft); color: var(--indigo); font-weight: 600; }
	.nav-dot { width: 6px; height: 6px; border-radius: 50%; background: currentColor; opacity: 0.4; flex-shrink: 0; }
	.nav-item.active .nav-dot { opacity: 1; background: var(--sidebar-accent); }

	/* ── Main ── */
	.main { flex: 1; min-width: 0; padding: 28px 32px; overflow-y: auto; }

	/* ── Top bar ── */
	.topbar { display: flex; align-items: flex-start; justify-content: space-between; margin-bottom: 20px; }
	.topbar-title { font-size: 18px; font-weight: 700; color: var(--text-primary); }
	.topbar-sub { font-size: 12px; color: var(--text-secondary); margin-top: 2px; font-family: 'DM Mono', monospace; }
	.topbar-actions { display: flex; gap: 8px; align-items: center; }

	select.ctrl { background: var(--surface); border: 1px solid var(--border); color: var(--text-primary); padding: 7px 11px; border-radius: var(--radius); font-size: 12px; font-family: 'Plus Jakarta Sans', sans-serif; cursor: pointer; box-shadow: var(--shadow); outline: none; }
	select.ctrl:focus { border-color: var(--indigo); }

	.btn { padding: 7px 14px; border-radius: var(--radius); font-size: 12px; font-weight: 600; font-family: 'Plus Jakarta Sans', sans-serif; cursor: pointer; border: none; transition: all 0.15s; }
	.btn-primary { background: var(--indigo); color: #fff; }
	.btn-primary:hover { background: #4f46e5; }
	.btn-primary:disabled { opacity: 0.55; cursor: not-allowed; }
	.btn-ghost { background: var(--surface); color: var(--text-secondary); border: 1px solid var(--border); box-shadow: var(--shadow); }
	.btn-ghost:hover { color: var(--text-primary); border-color: var(--gray-300); }

	/* ── Episode stat bar ── */
	.stat-row { display: flex; gap: 10px; margin-bottom: 18px; align-items: stretch; }
	.stat-tile { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 10px 14px; box-shadow: var(--shadow); min-width: 80px; }
	.stat-label { font-size: 10px; font-weight: 600; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.07em; margin-bottom: 4px; }
	.stat-val { font-size: 18px; font-weight: 700; font-family: 'DM Mono', monospace; }

	.budget-tile { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 10px 14px; flex: 1; box-shadow: var(--shadow); }
	.budget-top { display: flex; justify-content: space-between; font-size: 10px; font-weight: 600; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.07em; margin-bottom: 8px; }
	.budget-track { height: 5px; background: var(--gray-200); border-radius: 3px; overflow: hidden; }
	.budget-fill { height: 100%; border-radius: 3px; background: var(--indigo); transition: width 0.4s, background 0.3s; }

	/* ── Worker grid ── */
	.workers-grid { display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 18px; }
	.worker-card { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 13px; box-shadow: var(--shadow); cursor: pointer; transition: all 0.2s; position: relative; }
	.worker-card:hover { box-shadow: var(--shadow-md); border-color: var(--gray-300); }
	.worker-card.sel { border-color: var(--indigo); box-shadow: 0 0 0 3px var(--indigo-soft); }
	.worker-card.st-running { border-color: var(--indigo-border); background: var(--indigo-soft); }
	.worker-card.st-anomaly_detected { border-color: #fca5a5; background: var(--rose-soft); animation: apulse 1.5s infinite; }
	.worker-card.st-completed { border-color: #6ee7b7; background: var(--emerald-soft); opacity: 0.85; }
	.worker-card.st-intervened { border-color: #fcd34d; background: var(--amber-soft); }
	.worker-card.st-paused { opacity: 0.6; }
	@keyframes apulse { 0%,100% { box-shadow: 0 0 0 2px #fca5a5; } 50% { box-shadow: 0 0 0 4px #fca5a5; } }

	.w-flag { position: absolute; top: 10px; right: 10px; width: 7px; height: 7px; border-radius: 50%; }
	.f0 { background: var(--gray-200); }
	.f1 { background: var(--rose); animation: blink 1s infinite; }
	@keyframes blink { 0%,100% { opacity:1; } 50% { opacity:0.2; } }

	.w-num { font-size: 10px; color: var(--text-secondary); font-weight: 600; letter-spacing: 0.06em; text-transform: uppercase; margin-bottom: 5px; }
	.w-name { font-size: 12px; font-weight: 700; margin-bottom: 9px; color: var(--text-primary); line-height: 1.3; }

	.w-badge { display: inline-flex; align-items: center; gap: 4px; padding: 2px 7px; border-radius: 20px; font-size: 10px; font-weight: 600; margin-bottom: 9px; border: 1px solid; }
	.b-idle { background: var(--gray-100); color: var(--gray-400); border-color: var(--gray-200); }
	.b-running { background: var(--indigo-soft); color: var(--indigo); border-color: var(--indigo-border); }
	.b-healthy { background: var(--emerald-soft); color: var(--emerald); border-color: #a7f3d0; }
	.b-anomaly_detected { background: var(--rose-soft); color: var(--rose); border-color: #fca5a5; }
	.b-intervened { background: var(--amber-soft); color: var(--amber); border-color: #fcd34d; }
	.b-completed { background: var(--emerald-soft); color: var(--emerald); border-color: #a7f3d0; }
	.b-paused { background: var(--gray-100); color: var(--text-secondary); border-color: var(--gray-200); }
	.b-dot { width: 5px; height: 5px; border-radius: 50%; background: currentColor; flex-shrink: 0; }
	.b-dot.pulse { animation: blink 1.5s infinite; }

	.wm { display: flex; justify-content: space-between; font-size: 11px; padding: 2.5px 0; border-bottom: 1px solid var(--border-soft); }
	.wm:last-child { border-bottom: none; }
	.wm-k { color: var(--text-secondary); }
	.wm-v { font-family: 'DM Mono', monospace; color: var(--text-mono); font-size: 11px; }
	.wm-v.red { color: var(--rose); }
	.wm-v.amber { color: var(--amber); }
	.wm-v.green { color: var(--emerald); }
	.wm-v.indigo { color: var(--indigo); }

	/* ── Action panel ── */
	.panel { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 18px; box-shadow: var(--shadow); }
	.panel-title { font-size: 11px; font-weight: 700; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.09em; margin-bottom: 14px; }
	.field-label { font-size: 10px; font-weight: 600; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.07em; margin-bottom: 7px; }

	.action-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 6px; margin-bottom: 14px; }
	.act { padding: 8px 6px; border-radius: var(--radius-sm); font-size: 11px; font-weight: 600; cursor: pointer; border: 1px solid var(--border); background: var(--gray-100); color: var(--text-secondary); transition: all 0.15s; text-align: center; user-select: none; font-family: 'Plus Jakarta Sans', sans-serif; }
	.act:hover { background: var(--indigo-soft); border-color: var(--indigo-border); color: var(--indigo); }
	.act.on { background: var(--indigo-soft); border-color: var(--indigo); color: var(--indigo); }
	.act.danger:hover { background: var(--rose-soft); border-color: #fca5a5; color: var(--rose); }
	.act.danger.on { background: var(--rose-soft); border-color: #fca5a5; color: var(--rose); }
	.act.span3 { grid-column: span 3; }

	.chips { display: flex; gap: 5px; flex-wrap: wrap; margin-bottom: 12px; }
	.chip { padding: 3px 10px; border-radius: 20px; font-size: 11px; font-weight: 600; cursor: pointer; border: 1px solid var(--border); background: var(--gray-100); color: var(--text-secondary); transition: all 0.15s; user-select: none; }
	.chip:hover { background: var(--indigo-soft); border-color: var(--indigo-border); color: var(--indigo); }
	.chip.on { background: var(--indigo-soft); border-color: var(--indigo); color: var(--indigo); }
	.chip.alrt { border-color: #fca5a5; color: var(--rose); background: var(--rose-soft); }

	.reason-input { width: 100%; background: var(--gray-100); border: 1px solid var(--border); color: var(--text-primary); padding: 8px 10px; border-radius: var(--radius-sm); font-size: 12px; font-family: 'Plus Jakarta Sans', sans-serif; resize: none; margin-bottom: 10px; line-height: 1.5; }
	.reason-input:focus { outline: none; border-color: var(--indigo); background: #fff; }
	.reason-input::placeholder { color: var(--gray-300); }

	.exec-btn { width: 100%; padding: 9px; background: var(--indigo); color: #fff; border: none; border-radius: var(--radius-sm); font-size: 13px; font-weight: 600; font-family: 'Plus Jakarta Sans', sans-serif; cursor: pointer; transition: background 0.15s; }
	.exec-btn:hover:not(:disabled) { background: #4f46e5; }
	.exec-btn:disabled { background: var(--gray-200); color: var(--gray-400); cursor: not-allowed; }

	/* Reward box */
	.rw-box { margin-top: 11px; padding: 10px 12px; background: var(--gray-100); border: 1px solid var(--border); border-radius: var(--radius-sm); }
	.rw-row { display: flex; justify-content: space-between; font-size: 11px; font-family: 'DM Mono', monospace; padding: 2px 0; }
	.rw-k { color: var(--text-secondary); }
	.rw-pos { color: var(--emerald); }
	.rw-neg { color: var(--rose); }
	.rw-zero { color: var(--gray-300); }
	.rw-total { border-top: 1px solid var(--border); margin-top: 5px; padding-top: 5px; }
	.rw-total .rw-k { color: var(--text-primary); font-weight: 600; }

	/* ── Log panel ── */
	.log-panel { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 18px; box-shadow: var(--shadow); display: flex; flex-direction: column; }
	.log-scroll { flex: 1; height: 300px; overflow-y: auto; font-family: 'DM Mono', monospace; font-size: 11px; }
	.log-scroll::-webkit-scrollbar { width: 3px; }
	.log-scroll::-webkit-scrollbar-track { background: transparent; }
	.log-scroll::-webkit-scrollbar-thumb { background: var(--gray-200); border-radius: 2px; }
	.ll { padding: 3px 0; display: flex; gap: 7px; align-items: baseline; border-bottom: 1px solid var(--border-soft); }
	.ll-idx { color: var(--gray-300); width: 26px; flex-shrink: 0; }
	.ll-act { color: var(--indigo); min-width: 72px; flex-shrink: 0; }
	.ll-wkr { color: var(--gray-400); flex-shrink: 0; }
	.ll-rwd { margin-left: auto; flex-shrink: 0; }
	.ll-sys { color: var(--gray-400); font-style: italic; }
	.ll-err { color: var(--rose); }
	.ll-flag { color: var(--rose); font-weight: 700; }

	/* ── Eval panel ── */
	.eval-panel { margin-top: 12px; background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 20px; box-shadow: var(--shadow); }
	.eval-hdr { display: flex; justify-content: space-between; align-items: center; margin-bottom: 16px; }
	.eval-title { font-size: 14px; font-weight: 700; }
	.eval-badge { padding: 4px 12px; border-radius: 20px; font-size: 11px; font-weight: 700; border: 1px solid; }
	.badge-ok { background: var(--emerald-soft); color: var(--emerald); border-color: #a7f3d0; }
	.badge-fail { background: var(--rose-soft); color: var(--rose); border-color: #fca5a5; }

	.eval-scores { display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px; margin-bottom: 16px; }
	.ev-score { background: var(--gray-100); border: 1px solid var(--border); border-radius: var(--radius-sm); padding: 12px; text-align: center; }
	.ev-val { font-size: 22px; font-weight: 700; font-family: 'DM Mono', monospace; }
	.ev-lbl { font-size: 10px; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.06em; margin-top: 4px; }

	.gate-tbl { width: 100%; border-collapse: collapse; font-size: 12px; }
	.gate-tbl th { color: var(--text-secondary); text-transform: uppercase; font-size: 10px; font-weight: 600; letter-spacing: 0.07em; padding: 6px 8px; text-align: left; border-bottom: 1px solid var(--border); }
	.gate-tbl td { padding: 7px 8px; border-bottom: 1px solid var(--border-soft); }
	.gate-tbl td:first-child { color: var(--text-primary); font-size: 12px; }
	.gate-tbl td:nth-child(2), .gate-tbl td:nth-child(3) { font-family: 'DM Mono', monospace; color: var(--text-mono); font-size: 11px; }
	.g-pass { color: var(--emerald); font-weight: 600; }
	.g-fail { color: var(--rose); font-weight: 600; }

	/* ── FEATURE 1 & 2 ── */
	.runner-grid-3 { display: grid; grid-template-columns: 320px 1fr 300px; gap: 16px; align-items: start; margin-top: 20px; }
	.col-stack { display: flex; flex-direction: column; gap: 16px; min-width: 0; }

	.strategy-guide { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 18px; box-shadow: var(--shadow); height: fit-content; }
	.guide-label { font-family: 'DM Mono', monospace; font-size: 10px; font-weight: 700; color: var(--text-secondary); text-transform: uppercase; margin-top: 16px; margin-bottom: 8px; border-bottom: 1px solid var(--border-soft); padding-bottom: 4px; }
	.guide-label:first-child { margin-top: 0; }
	.guide-table { width: 100%; border-collapse: collapse; font-size: 11px; }
	.guide-table td { padding: 5px 0; vertical-align: top; border-bottom: 1px solid var(--border-soft); }
	.guide-table tr:last-child td { border-bottom: none; }
	.guide-table .act-name { font-family: 'DM Mono', monospace; font-weight: 600; color: var(--indigo); width: 85px; }
	.guide-table .act-desc { color: var(--text-secondary); padding-right: 8px; font-size: 10px; line-height: 1.3; }
	.guide-table .act-reward { font-family: 'DM Mono', monospace; text-align: right; white-space: nowrap; font-weight: 500; }
	.guide-list { list-style: none; padding: 0; }
	.guide-list li { font-size: 11px; margin-bottom: 6px; color: var(--text-secondary); position: relative; padding-left: 14px; line-height: 1.4; }
	.guide-list li::before { content: "•"; position: absolute; left: 0; color: var(--indigo); font-weight: bold; }
	.guide-tip-val { color: var(--text-primary); font-weight: 600; }

	.hint-panel { background: #eef2ff; border: 1px solid #c7d2fe; border-radius: var(--radius); padding: 12px 16px; transition: opacity 0.3s ease, transform 0.3s ease; }
	.hint-title { font-family: 'DM Mono', monospace; font-size: 10px; font-weight: 700; color: var(--indigo); text-transform: uppercase; margin-bottom: 4px; display: flex; align-items: center; gap: 6px; }
	.hint-content { font-size: 12px; color: var(--text-primary); line-height: 1.4; }

	/* ── Multi-screen ── */
	.screen { display: none; }
	.screen.active { display: block; }

	/* ── Planning Phase ── */
	.card { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); box-shadow: var(--shadow); margin-bottom: 20px; overflow: hidden; }
	.card-header { padding: 14px 18px; border-bottom: 1px solid var(--border-soft); display: flex; align-items: center; gap: 8px; }
	.card-title { font-size: 14px; font-weight: 700; color: var(--text-primary); }
	.card-sub { font-size: 11px; color: var(--text-secondary); font-family: 'DM Mono', monospace; }
	.card-body { padding: 18px; }

	.tag { padding: 2px 8px; border-radius: 4px; font-size: 10px; font-weight: 600; text-transform: uppercase; border: 1px solid; }
	.playground-side-title { font-size: 10px; font-weight: 700; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.05em; margin-bottom: 8px; }
	.obs-box { background: #1e1e1e; color: #d4d4d4; padding: 12px; border-radius: var(--radius-sm); font-family: 'DM Mono', monospace; white-space: pre-wrap; word-break: break-all; }
	.obs-key { color: #9cdcfe; }
	.obs-val { color: #ce9178; }
	.obs-num { color: #b5cea8; }

	.playground-input { background: var(--gray-100); border: 1px solid var(--border); padding: 6px 8px; border-radius: var(--radius-sm); font-size: 12px; width: 100%; outline: none; }
	.playground-input:focus { border-color: var(--indigo); background: #fff; }

	.worker-card.healthy { border-color: #6ee7b7; background: var(--emerald-soft); }
	.worker-card.anomaly { border-color: #fca5a5; background: var(--rose-soft); }
	.worker-card.selected { border-color: var(--indigo); box-shadow: 0 0 0 3px var(--indigo-soft); }
	.worker-number { font-size: 10px; font-weight: 600; color: var(--text-secondary); margin-bottom: 2px; }

	/* ── Transfer Demo ── */
	.metrics-row { display: grid; grid-template-columns: repeat(4, 1fr); gap: 12px; }
	.metric-card { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 14px; text-align: center; }
	.metric-label { font-size: 10px; font-weight: 600; color: var(--text-secondary); text-transform: uppercase; margin-bottom: 6px; }
	.metric-value { font-size: 20px; font-weight: 700; margin-bottom: 4px; }
	.metric-delta { font-size: 10px; }
	.step-log { background: #1e1e1e; border-radius: var(--radius-sm); padding: 12px; font-family: 'DM Mono', monospace; font-size: 11px; overflow-y: auto; color: #d4d4d4; }
	.log-line { display: flex; gap: 10px; margin-bottom: 4px; border-bottom: 1px solid #333; padding-bottom: 2px; }
	.log-ts { color: #858585; min-width: 65px; }
	.log-action { color: #569cd6; min-width: 100px; }
	.log-result-ok { color: #4ec9b0; }
	.log-result-err { color: #f44747; }
	.log-result-warn { color: #dcdcaa; }
	.chat-input { flex: 1; padding: 10px 14px; border-radius: var(--radius); border: 1px solid var(--border); outline: none; }
	.chat-input:focus { border-color: var(--indigo); }
	.chat-msg { display: flex; gap: 12px; margin-bottom: 12px; }
	.chat-avatar { width: 32px; height: 32px; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-size: 16px; flex-shrink: 0; }
	.chat-avatar.user { background: var(--gray-100); }
	.chat-avatar.bot { background: var(--indigo-soft); }
	.chat-bubble { padding: 10px 14px; border-radius: 12px; font-size: 13px; max-width: 80%; line-height: 1.5; }
	.chat-msg.user .chat-bubble { background: var(--gray-100); border-top-left-radius: 2px; }
	.chat-msg.bot .chat-bubble { background: var(--surface); border: 1px solid var(--border); border-top-right-radius: 2px; }

	.playground-kv { display: flex; justify-content: space-between; padding: 6px 0; border-bottom: 1px solid var(--border-soft); font-size: 12px; }
	.playground-kv:last-child { border-bottom: none; }
	.playground-kv-key { color: var(--text-secondary); }
	.playground-kv-val { font-weight: 600; font-family: 'DM Mono', monospace; }

	/* ── Extra Screen Styles ── */
	.hero { margin-bottom: 40px; margin-top: 20px; }
	.hero-badge { display: inline-flex; align-items: center; gap: 7px; background: var(--indigo-soft); border: 1px solid var(--indigo-border); padding: 4px 12px; border-radius: 20px; font-size: 11px; color: var(--indigo); font-weight: 600; margin-bottom: 18px; }
	.badge-dot { width: 5px; height: 5px; border-radius: 50%; background: var(--indigo); animation: blink 2s infinite; }
	.hero h1 { font-size: 32px; font-weight: 700; line-height: 1.2; margin-bottom: 14px; color: var(--text-primary); }
	.hero h1 span { color: var(--indigo); }
	.hero p { font-size: 14px; color: var(--text-secondary); max-width: 560px; line-height: 1.7; margin-bottom: 26px; }
	.hero-actions { display: flex; gap: 10px; }

	.stats-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 12px; margin-bottom: 32px; }
	.stat-card { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 16px; box-shadow: var(--shadow); }
	.stat-label { font-size: 10px; color: var(--text-secondary); font-weight: 600; text-transform: uppercase; letter-spacing: 0.08em; margin-bottom: 8px; }
	.stat-value { font-size: 26px; font-weight: 700; font-family: 'DM Mono', monospace; color: var(--text-primary); }
	.stat-value.indigo { color: var(--indigo); }
	.stat-value.emerald { color: var(--emerald); }
	.stat-value.amber { color: var(--amber); }
	.stat-sub { font-size: 11px; color: var(--gray-400); margin-top: 4px; }

	.features-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 16px; }
	.feature-card { background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 20px; cursor: pointer; transition: all 0.18s; box-shadow: var(--shadow); }
	.feature-card:hover { box-shadow: var(--shadow-md); border-color: var(--gray-300); transform: translateY(-1px); }
	.feature-icon { width: 38px; height: 38px; border-radius: 8px; display: flex; align-items: center; justify-content: center; font-size: 16px; margin-bottom: 12px; }
	.fi-indigo { background: var(--indigo-soft); }
	.fi-emerald { background: var(--emerald-soft); }
	.fi-amber { background: var(--amber-soft); }
	.feature-title { font-size: 14px; font-weight: 700; margin-bottom: 6px; }
	.feature-desc { font-size: 12px; color: var(--text-secondary); line-height: 1.6; }

	.health-bar { display: flex; align-items: center; gap: 8px; padding: 9px 14px; border-radius: var(--radius); margin-bottom: 20px; font-size: 12px; font-weight: 500; border: 1px solid; }
	.health-bar.ok { background: var(--emerald-soft); border-color: #a7f3d0; color: var(--emerald); }
	.health-bar.err { background: var(--rose-soft); border-color: #fca5a5; color: var(--rose); }
	.health-bar.checking { background: var(--gray-100); border-color: var(--gray-200); color: var(--text-secondary); }
	.h-dot { width: 7px; height: 7px; border-radius: 50%; background: currentColor; }

	.chat-header { padding: 16px 0; border-bottom: 1px solid var(--border); display: flex; align-items: center; justify-content: space-between; }
	.pipe-badge { display: flex; align-items: center; gap: 6px; padding: 4px 10px; border-radius: 20px; font-size: 11px; font-weight: 600; border: 1px solid; cursor: pointer; }
	.pipe-ready { background: var(--emerald-soft); color: var(--emerald); border-color: #a7f3d0; }
	.pipe-not-ready { background: var(--amber-soft); color: var(--amber); border-color: #fcd34d; }
	.pipe-checking { background: var(--gray-100); color: var(--text-secondary); border-color: var(--gray-200); }
	.pipe-dot { width: 6px; height: 6px; border-radius: 50%; background: currentColor; }

	.welcome { text-align: center; padding: 28px 0; }
	.sq { padding: 5px 13px; background: var(--surface); border: 1px solid var(--border); border-radius: 20px; font-size: 12px; color: var(--text-secondary); cursor: pointer; transition: all 0.15s; }
	.sq:hover { border-color: var(--indigo-border); color: var(--indigo); background: var(--indigo-soft); }

	.page-header { margin-bottom: 24px; }
	.page-header h1 { font-size: 24px; font-weight: 700; margin-bottom: 4px; }
	.page-header p { font-size: 13px; color: var(--text-secondary); }

	.base-url-bar { display: flex; align-items: center; gap: 10px; background: var(--surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 10px 14px; margin-bottom: 24px; box-shadow: var(--shadow); }
	.base-url-label { font-size: 10px; font-weight: 600; color: var(--text-secondary); text-transform: uppercase; letter-spacing: 0.08em; }
	.base-url-value { font-family: 'DM Mono', monospace; font-size: 13px; color: var(--indigo); }

	.status-banner { display: flex; align-items: center; gap: 16px; padding: 20px 24px; border-radius: 10px; margin-bottom: 24px; border: 1px solid; }
	.status-banner.ok { background: var(--emerald-soft); border-color: #a7f3d0; }
	.status-banner.err { background: var(--rose-soft); border-color: #fca5a5; }

	.kv-row { display: flex; align-items: center; justify-content: space-between; padding: 6px 0; border-bottom: 1px solid var(--border-soft); }
	.kv-key { font-size: 12px; color: var(--text-secondary); }
	.kv-val { font-family: 'DM Mono', monospace; font-size: 12px; font-weight: 600; }

	.ep-health-row { display: flex; align-items: center; gap: 10px; padding: 8px 0; border-bottom: 1px solid var(--border-soft); }
	.ep-path { font-family: 'DM Mono', monospace; font-size: 12px; flex: 1; }
	.method-badge { font-family: 'DM Mono', monospace; font-size: 10px; font-weight: 600; padding: 2px 6px; border-radius: 4px; min-width: 40px; text-align: center; }
	.method-get { background: var(--emerald-soft); color: var(--emerald); border: 1px solid #a7f3d0; }
	.method-post { background: var(--indigo-soft); color: var(--indigo); border: 1px solid var(--indigo-border); }

	@media (max-width: 1400px) {
	.runner-grid-3 { grid-template-columns: 1fr 300px; }
	.runner-grid-3 > .col-stack:first-child { grid-column: span 2; order: 3; }
	}
	@media (max-width: 1100px) {
	.runner-grid-3 { grid-template-columns: 1fr; }
	.runner-grid-3 > .col-stack:first-child { grid-column: span 1; order: 3; }
	}
	</style>
	</head>
	<body>
	<div class="app-shell" id="screen-fleet">
	<nav class="sidebar">
	<div class="sidebar-logo">
	<div class="logo-mark">⚡</div>
	<div class="logo-text">Fleet Oversight<span>v2.0 · Hackathon Finals</span></div>
	</div>
	<div class="nav-section">Workspace</div>
	<div class="nav-item active" onclick="showScreen('dashboard')"><div class="nav-dot"></div>Overview</div>
	<div class="nav-item" onclick="showScreen('runner')"><div class="nav-dot"></div>Fleet Runner</div>
	<div class="nav-item" onclick="showScreen('audit')"><div class="nav-dot"></div>Audit Report</div>
	<div class="nav-item" onclick="showScreen('chat')"><div class="nav-dot"></div>RAG Chatbot</div>
	<div class="nav-item" onclick="showScreen('transfer')"><div class="nav-dot"></div>Transfer Demo</div>
	<div class="nav-section">Evaluation</div>
	<div class="nav-item" onclick="showScreen('training')"><div class="nav-dot"></div>Training Results</div>
	<div class="nav-item" onclick="showScreen('blog')"><div class="nav-dot"></div>Project Blog</div>
	<div class="nav-section">API</div>
	<div class="nav-item" onclick="showScreen('api')"><div class="nav-dot"></div>API Docs</div>
	<div class="nav-item" onclick="showScreen('health')"><div class="nav-dot"></div>Health Check</div>
	</nav>

	<main class="main">
	<div class="topbar">
	<div>
	<div class="topbar-title">Overview</div>
	<div class="topbar-sub">Enterprise AI governance story · why this matters now</div>
	</div>
	<div class="topbar-actions" id="runner-actions" style="display:none; flex-direction:column; align-items:flex-end;">
	<div style="display:flex; gap:8px; align-items:center;">
	<select class="ctrl" id="task-sel">
	<option value="easy_fleet">Easy — 1 anomaly · 8 steps</option>
	<option value="medium_fleet">Medium — 2 anomalies · 12 steps</option>
	<option value="hard_fleet">Hard — 3 anomalies · 16 steps</option>
	<option value="very_hard_fleet">Very Hard — 4 anomalies · 20 steps</option>
	<option value="impossible_fleet">Impossible — 5 anomalies · 24 steps</option>
	</select>
	<button class="btn btn-primary" id="btn-start" onclick="startEpisode()">▶ New Episode</button>
	<button class="btn btn-ghost" id="btn-auto" onclick="autoRunEpisode()">⚡ Auto Run (Full Episode)</button>
	</div>
	<div style="font-size: 11px; color: var(--text-secondary); margin-top: 4px;">
	New Episode = manual play · Auto Run = watch full automated episode
	</div>
	</div>
	</div>

	<!-- DASHBOARD SCREEN -->
	<div id="screen-dashboard" class="screen active">
	<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 32px;">
	<div style="display: flex; gap: 8px; align-items: center;">
	<div class="health-badge" id="health-badge" style="display: flex; align-items: center; gap: 8px; padding: 6px 16px; background: #ecfdf5; border: 1px solid #a7f3d0; border-radius: 100px; color: #059669; font-size: 12px; font-weight: 600;">
	<div style="width: 8px; height: 8px; background: #10b981; border-radius: 50%; animation: blink 2s infinite;"></div>
	All Systems Operational
	</div>
	<div style="padding: 6px 16px; background: #eff6ff; border: 1px solid #bfdbfe; border-radius: 100px; color: #2563eb; font-size: 12px; font-weight: 600;">
	Reinforcement Learning
	</div>
	</div>
	<button class="btn btn-primary" style="padding: 10px 24px; border-radius: 100px;" onclick="showScreen('runner')">▶ Run Demo</button>
	</div>

	<!-- Hero Section -->
	<div class="card" style="padding: 48px; display: flex; align-items: center; gap: 40px; margin-bottom: 32px; border: none; box-shadow: var(--shadow-md);">
	<div style="flex: 1.2;">
	<div style="padding: 4px 12px; background: #eff6ff; border: 1px solid #bfdbfe; border-radius: 100px; color: #2563eb; font-size: 11px; font-weight: 700; display: inline-block; margin-bottom: 24px;">Enterprise AI Governance</div>
	<h1 style="font-size: 40px; font-weight: 800; color: #0f172a; line-height: 1.1; margin-bottom: 20px;">The missing governance layer<br>for agentic AI.</h1>
	<p style="font-size: 16px; color: #64748b; line-height: 1.6; margin-bottom: 32px;">Most enterprises optimize AI generation quality. Few govern the multi-agent workflows running it. Fleet Oversight is an RL-trained oversight agent that supervises your RAG pipeline, detects anomalies, and produces auditable evidence — automatically.</p>
	<div style="display: flex; gap: 16px; align-items: center;">
	<button class="btn btn-primary" style="padding: 12px 32px; border-radius: 8px; font-size: 14px;" onclick="showScreen('runner')">▶ Run Live Demo</button>
	<a href="#" onclick="showScreen('audit')" style="color: #64748b; font-weight: 600; text-decoration: none; display: flex; align-items: center; gap: 6px; font-size: 14px;">View Audit Report →</a>
	</div>
	</div>
	<div style="flex: 1; text-align: center;">
	<img src="/static/fleet_architecture.png" style="max-width: 100%; border-radius: 12px; box-shadow: 0 20px 40px rgba(0,0,0,0.3);" alt="Architecture Diagram">
	</div>
	</div>

	<!-- Stats Row -->
	<div style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 24px; margin-bottom: 32px;">
	<div class="card" style="padding: 24px; border: none; box-shadow: var(--shadow);">
	<div style="font-size: 12px; color: #64748b; font-weight: 600; text-transform: uppercase; margin-bottom: 12px;">Detection Rate</div>
	<div style="font-size: 36px; font-weight: 800; color: #0f172a;">87%</div>
	<div style="font-size: 13px; color: #10b981; font-weight: 600; margin-top: 8px;">↑ +46pp vs random baseline</div>
	</div>
	<div class="card" style="padding: 24px; border: none; box-shadow: var(--shadow);">
	<div style="font-size: 12px; color: #64748b; font-weight: 600; text-transform: uppercase; margin-bottom: 12px;">Composite Governance Score</div>
	<div style="font-size: 36px; font-weight: 800; color: #0f172a;">0.73</div>
	<div style="font-size: 13px; color: #10b981; font-weight: 600; margin-top: 8px;">✓ All 5 governance gates passed</div>
	</div>
	<div class="card" style="padding: 24px; border: none; box-shadow: var(--shadow);">
	<div style="font-size: 12px; color: #64748b; font-weight: 600; text-transform: uppercase; margin-bottom: 12px;">Transfer Domains</div>
	<div style="font-size: 36px; font-weight: 800; color: #0f172a;">2x</div>
	<div style="font-size: 13px; color: #64748b; font-weight: 600; margin-top: 8px;">CRM → Banking, zero retraining</div>
	</div>
	</div>

	<!-- Problem/Solution Grid -->
	<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 24px; margin-bottom: 32px;">
	<div class="card" style="padding: 24px; border: none; box-shadow: var(--shadow);">
	<div style="font-size: 16px; font-weight: 800; color: #0f172a; margin-bottom: 20px;">The problem</div>
	<div style="display: flex; flex-direction: column; gap: 16px;">
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #ef4444; font-weight: bold;">!</span> Workers drift from intended behavior without active supervision
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #ef4444; font-weight: bold;">!</span> Errors compound silently across pipeline stages — no alert fires
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #ef4444; font-weight: bold;">!</span> Budget leaks through bad routing, false approvals, missed violations
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #ef4444; font-weight: bold;">!</span> No audit trail to prove why a run passed or failed
	</div>
	</div>
	</div>
	<div class="card" style="padding: 24px; border: none; box-shadow: var(--shadow);">
	<div style="font-size: 16px; font-weight: 800; color: #0f172a; margin-bottom: 20px;">Our solution</div>
	<div style="display: flex; flex-direction: column; gap: 16px;">
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #10b981; font-weight: bold;">✓</span> RL-trained agent learns to plan tasks and intervene with precision
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #10b981; font-weight: bold;">✓</span> Real-time anomaly detection across all 5 RAG pipeline stages
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #10b981; font-weight: bold;">✓</span> Budget-aware action selection: monitor, intervene, escalate, hold
	</div>
	<div style="display: flex; gap: 12px; align-items: flex-start; font-size: 14px; color: #64748b;">
	<span style="color: #10b981; font-weight: bold;">✓</span> Structured audit report per episode — transferable across domains
	</div>
	</div>
	</div>
	</div>

	<!-- Footer Bar -->
	<div style="padding: 16px 24px; background: #eff6ff; border: 1px solid #bfdbfe; border-radius: 8px; display: flex; justify-content: space-between; align-items: center;">
	<div>
	<div style="font-size: 14px; font-weight: 800; color: #1e40af;">Built for enterprise RAG pipelines. Trained. Transferable. Auditable.</div>
	<div style="font-size: 12px; color: #64748b; margin-top: 2px;">Two-phase RL environment · GRPO training · 5 governance gates · Zero-shot domain transfer</div>
	</div>
	<button class="btn btn-primary" style="background: #2563eb; padding: 8px 20px; border-radius: 6px; font-size: 13px;" onclick="showScreen('runner')">See it in action →</button>
	</div>
	</div>

	<!-- FLEET RUNNER SCREEN -->
	<div id="screen-runner" class="screen">

	<!-- Stats bar -->
	<div class="stat-row">
	<div class="stat-tile">
	<div class="stat-label">Step</div>
	<div class="stat-val" id="s-step" style="color:var(--indigo)">—</div>
	</div>
	<div class="budget-tile">
	<div class="budget-top"><span>Oversight Budget</span><span id="s-budget-txt" style="font-family:'DM Mono',monospace">—</span></div>
	<div class="budget-track"><div class="budget-fill" id="s-budget-bar" style="width:100%"></div></div>
	</div>
	<div class="stat-tile">
	<div class="stat-label">Total Reward</div>
	<div class="stat-val" id="s-reward" style="color:var(--text-secondary)">—</div>
	</div>
	<div class="stat-tile">
	<div class="stat-label">Alerts</div>
	<div class="stat-val" id="s-alerts" style="color:var(--text-secondary)">—</div>
	</div>
	<div class="stat-tile">
	<div class="stat-label">Episode</div>
	<div class="stat-val" id="s-status" style="font-size:12px;color:var(--text-secondary)">IDLE</div>
	<div id="ep-sub" style="font-size:9px;color:var(--text-secondary);margin-top:2px"></div>
	</div>
	</div>

	<div class="runner-grid-3">
	<!-- Left Column: Episode Log -->
	<div class="col-stack">
	<div class="log-panel" style="flex:1; min-height: 500px;">
	<div class="panel-title" style="margin-bottom:10px">Episode Log</div>
	<div class="log-scroll" id="runner-step-log"></div>
	</div>
	</div>

	<!-- Center Column: Workers, Hints, Actions -->
	<div class="col-stack">
	<!-- PLANNING PHASE CARD — shows during planning, hides during oversight -->
	<div id="planning-phase-card" class="card" style="margin-bottom:20px; display: none;">
	<div class="card-header">
	<div class="card-title">📋 Phase 1 — Task Allocation</div>
	<span class="tag" style="background:var(--indigo-soft);border-color:var(--indigo-border);color:var(--indigo)">● Planning</span>
	<span class="tag" id="planning-budget-tag" style="margin-left:4px">Budget: 5</span>
	<div class="card-sub" id="planning-progress-tag">0 / 5 workers allocated</div>
	</div>
	<div class="card-body" style="padding:16px">

	<!-- Dataset Profile -->
	<div style="margin-bottom:16px">
	<div class="playground-side-title">Dataset Profile — Read this to decide allocations</div>
	<div id="dataset-profile-box" class="obs-box" style="font-size:11px;line-height:1.8">
	Start an episode to see dataset characteristics
	</div>
	</div>

	<!-- Allocation Grid -->
	<div style="margin-bottom:14px">
	<div class="playground-side-title">Allocate Tasks to Workers</div>
	<div style="display:grid;grid-template-columns:repeat(5,1fr);gap:10px;margin-top:8px" id="allocation-grid">

	<!-- Worker 1 -->
	<div class="worker-card" id="alloc-worker_1">
	<div class="worker-number">Worker 01</div>
	<div class="worker-name">Data Clean</div>
	<select id="select-worker_1" class="playground-input" style="font-size:11px;margin-top:8px" onchange="allocateWorker('worker_1')" onblur="allocateWorker('worker_1')">
	<option value="">Select task...</option>
	<option value="easy_missing_and_dupes">Easy — Missing + Dupes</option>
	<option value="medium_type_and_category">Medium — Type + Category</option>
	<option value="hard_conflicts_and_budget">Hard — Conflicts + Budget</option>
	</select>
	<div id="alloc-reward-worker_1" style="font-size:10px;margin-top:6px;font-family:'DM Mono',monospace;color:var(--text-secondary)">not allocated</div>
	</div>

	<!-- Worker 2 -->
	<div class="worker-card" id="alloc-worker_2">
	<div class="worker-number">Worker 02</div>
	<div class="worker-name">Chunking</div>
	<select id="select-worker_2" class="playground-input" style="font-size:11px;margin-top:8px" onchange="allocateWorker('worker_2')" onblur="allocateWorker('worker_2')">
	<option value="">Select task...</option>
	<option value="easy_chunking">Easy Chunking</option>
	<option value="medium_chunking">Medium Chunking</option>
	<option value="hard_chunking">Hard Chunking</option>
	</select>
	<div id="alloc-reward-worker_2" style="font-size:10px;margin-top:6px;font-family:'DM Mono',monospace;color:var(--text-secondary)">not allocated</div>
	</div>

	<!-- Worker 3 -->
	<div class="worker-card" id="alloc-worker_3">
	<div class="worker-number">Worker 03</div>
	<div class="worker-name">Embedding</div>
	<select id="select-worker_3" class="playground-input" style="font-size:11px;margin-top:8px" onchange="allocateWorker('worker_3')" onblur="allocateWorker('worker_3')">
	<option value="">Select task...</option>
	<option value="easy_embedding">Easy Embedding</option>
	<option value="medium_embedding">Medium Embedding</option>
	<option value="hard_embedding">Hard Embedding</option>
	</select>
	<div id="alloc-reward-worker_3" style="font-size:10px;margin-top:6px;font-family:'DM Mono',monospace;color:var(--text-secondary)">not allocated</div>
	</div>

	<!-- Worker 4 -->
	<div class="worker-card" id="alloc-worker_4">
	<div class="worker-number">Worker 04</div>
	<div class="worker-name">Retrieval</div>
	<select id="select-worker_4" class="playground-input" style="font-size:11px;margin-top:8px" onchange="allocateWorker('worker_4')" onblur="allocateWorker('worker_4')">
	<option value="">Select task...</option>
	<option value="easy_retrieval">Easy Retrieval</option>
	<option value="medium_retrieval">Medium Retrieval</option>
	<option value="hard_retrieval">Hard Retrieval</option>
	</select>
	<div id="alloc-reward-worker_4" style="font-size:10px;margin-top:6px;font-family:'DM Mono',monospace;color:var(--text-secondary)">not allocated</div>
	</div>

	<!-- Worker 5 -->
	<div class="worker-card" id="alloc-worker_5">
	<div class="worker-number">Worker 05</div>
	<div class="worker-name">Evaluation</div>
	<select id="select-worker_5" class="playground-input" style="font-size:11px;margin-top:8px" onchange="allocateWorker('worker_5')" onblur="allocateWorker('worker_5')">
	<option value="">Select task...</option>
	<option value="easy_evaluation">Easy Evaluation</option>
	<option value="medium_evaluation">Medium Evaluation</option>
	<option value="hard_evaluation">Hard Evaluation</option>
	</select>
	<div id="alloc-reward-worker_5" style="font-size:10px;margin-top:6px;font-family:'DM Mono',monospace;color:var(--text-secondary)">not allocated</div>
	</div>

	</div>
	</div>

	<!-- Planning reward display -->
	<div id="planning-reward-display" style="display:flex;gap:12px;align-items:center;margin-bottom:14px">
	<div style="font-size:11px;color:var(--text-secondary)">Planning reward so far:</div>
	<div id="planning-reward-val" style="font-family:'DM Mono',monospace;font-size:13px;font-weight:600;color:var(--emerald)">+0.000</div>
	</div>

	<!-- Action buttons -->
	<div style="display:flex;gap:10px">
	<button class="btn btn-primary" style="flex:1" onclick="submitAllAllocations()">
	✓ Confirm All Allocations → Start Oversight
	</button>
	<button class="btn" onclick="autoAllocate()">
	⚡ Auto Allocate (Agent Default)
	</button>
	</div>

	<!-- Planning hint -->
	<div style="margin-top:12px;padding:10px 14px;background:var(--indigo-soft);border:1px solid var(--indigo-border);border-radius:var(--radius-sm);font-size:11px;color:#3730a3;line-height:1.6">
	💡 <strong>How to allocate:</strong> Read the dataset profile above. High missing_value_rate → use harder Data Clean task. High text_complexity → use hard Embedding. Low rates → easy tasks are sufficient and more efficient.
	</div>

	</div>
	</div>

	<div class="workers-grid" id="workers-grid" style="grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));"></div>

	<div id="hints-container" class="hint-panel">
	<div class="hint-title">💡 LIVE HINT</div>
	<div id="hint-text" class="hint-content">Select a difficulty above and click New Episode to begin. Then monitor workers to establish a baseline before intervening.</div>
	</div>

	<div class="panel">
	<div class="panel-title">Oversight Actions</div>

	<div class="field-label">Action Type</div>
	<div class="action-grid">
	<div class="act" id="btn-monitor" onclick="selAction('monitor')">👁 Monitor</div>
	<div class="act danger" id="btn-intervene" onclick="selAction('intervene')">🚨 Intervene</div>
	<div class="act" id="btn-approve" onclick="selAction('approve')">✓ Approve</div>
	<div class="act" id="btn-escalate" onclick="selAction('escalate')">⚠ Escalate</div>
	<div class="act" id="btn-pause" onclick="selAction('pause')">⏸ Pause</div>
	<div class="act" id="btn-resume" onclick="selAction('resume')">▶ Resume</div>
	<div class="act span3" id="btn-submit_audit" onclick="selAction('submit_audit')">📋 Submit Audit Report — ends episode</div>
	</div>

	<div class="field-label">Target Worker</div>
	<div class="chips" id="chips">
	<div class="chip" id="chip-worker_1" onclick="selWorker('worker_1')">W1: Data Clean</div>
	<div class="chip" id="chip-worker_2" onclick="selWorker('worker_2')">W2: Chunking</div>
	<div class="chip" id="chip-worker_3" onclick="selWorker('worker_3')">W3: Embedding</div>
	<div class="chip" id="chip-worker_4" onclick="selWorker('worker_4')">W4: Retrieval</div>
	<div class="chip" id="chip-worker_5" onclick="selWorker('worker_5')">W5: Evaluation</div>
	</div>

	<textarea class="reason-input" id="reason" rows="2" placeholder="Reason — contributes to explainability score"></textarea>
	<button class="exec-btn" id="exec-btn" onclick="execAction()" disabled>Execute Action</button>

	<div class="rw-box" id="rw-box" style="display:none">
	<div class="rw-row"><span class="rw-k">detection</span><span id="r-det" class="rw-zero">—</span></div>
	<div class="rw-row"><span class="rw-k">approval</span><span id="r-app" class="rw-zero">—</span></div>
	<div class="rw-row"><span class="rw-k">escalation</span><span id="r-esc" class="rw-zero">—</span></div>
	<div class="rw-row"><span class="rw-k">false_positive</span><span id="r-fp" class="rw-zero">—</span></div>
	<div class="rw-row"><span class="rw-k">missed_violation</span><span id="r-mv" class="rw-zero">—</span></div>
	<div class="rw-row"><span class="rw-k">completion</span><span id="r-comp" class="rw-zero">—</span></div>
	<div class="rw-row rw-total"><span class="rw-k">TOTAL</span><span id="r-total" class="rw-zero">—</span></div>
	</div>
	</div>
	</div>

	<!-- Right Column: Strategy Guide -->
	<div class="col-stack">
	<div class="strategy-guide">
	<div class="panel-title">Strategy Guide</div>

	<div class="guide-label">Actions & Rewards</div>
	<table class="guide-table">
	<tr><td class="act-name">monitor</td><td class="act-desc">Use when unsure. No reward. Avoid repeating.</td><td class="act-reward rw-zero">0.00</td></tr>
	<tr><td class="act-name">intervene</td><td class="act-desc">Use when confident fault detected.</td><td class="act-reward"><span class="rw-pos">+0.40</span> / <span class="rw-neg">-0.45</span></td></tr>
	<tr><td class="act-name">approve</td><td class="act-desc">Use to explicitly trust a healthy worker.</td><td class="act-reward"><span class="rw-pos">+0.10</span> / <span class="rw-neg">-0.65</span></td></tr>
	<tr><td class="act-name">escalate</td><td class="act-desc">Use when flag fires but you are unsure.</td><td class="act-reward rw-pos">+0.15</td></tr>
	<tr><td class="act-name">pause</td><td class="act-desc">Use to halt a suspicious worker.</td><td class="act-reward"><span class="rw-pos">+0.30</span> / <span class="rw-neg">-0.35</span></td></tr>
	<tr><td class="act-name">resume</td><td class="act-desc">Use to resume a paused worker.</td><td class="act-reward rw-pos">+0.08</td></tr>
	<tr><td class="act-name">submit_audit</td><td class="act-desc">Use to end episode with full audit report.</td><td class="act-reward rw-pos">+0.20</td></tr>
	</table>

	<div class="guide-label">Reading Worker Cards</div>
	<ul class="guide-list">
	<li>Green dot top right = <span class="guide-tip-val">anomaly_flag 0</span> (likely healthy)</li>
	<li>Red dot top right = <span class="guide-tip-val">anomaly_flag 1</span> (possibly faulty — but 20% are false alarms)</li>
	<li>Budget draining fast = <span class="guide-tip-val">possible drift anomaly</span></li>
	<li>Same action repeating = <span class="guide-tip-val">possible constraint violation</span></li>
	<li>Worker 3 and 4 acting in sync = <span class="guide-tip-val">possible collusion</span></li>
	</ul>

	<div class="guide-label">Difficulty Tips</div>
	<ul class="guide-list">
	<li><span class="guide-tip-val">Easy:</span> 1 anomaly, budget dump. Flag fires reliably. Catch it early.</li>
	<li><span class="guide-tip-val">Medium:</span> 2 anomalies. Noisy flags. Cross-reference budget drain with flag.</li>
	<li><span class="guide-tip-val">Hard:</span> 3 anomalies. Drift silent until step 6. Collusion between W3+W4. Trust patterns not flags.</li>
	</ul>

	<div class="guide-label">Winning Strategy</div>
	<ul class="guide-list">
	<li>1. Start with monitor on all 5 workers</li>
	<li>2. Look for budget draining faster than others</li>
	<li>3. Look for flag=1 consistently — not just once</li>
	<li>4. Intervene on high confidence. Escalate on uncertainty.</li>
	<li>5. Approve healthy workers to earn +0.10 each</li>
	<li>6. Submit audit before budget runs out</li>
	</ul>
	</div>
	</div>
	</div>

	</div>

	<!-- AUDIT REPORT SCREEN -->
	<div id="screen-audit" class="screen">
	<div style="margin-bottom:18px">
	<div style="font-size:11px;color:var(--indigo);font-weight:700;text-transform:uppercase;letter-spacing:0.08em;font-family:'DM Mono',monospace;margin-bottom:6px">Governance Evidence</div>
	<div style="font-size:20px;font-weight:700;margin-bottom:8px">Detailed Episode Audit Report</div>
	<div style="font-size:13px;color:var(--text-secondary);line-height:1.6">This page captures the complete oversight outcome for your latest run. Use it to tell the story of risk detection, intervention quality, and enterprise readiness.</div>
	</div>
	<div class="eval-panel" id="eval-panel" style="display:block">
	<div class="eval-hdr">
	<div class="eval-title" id="eval-title-text">Detailed Audit Report — Episode N/A</div>
	<div class="eval-badge" id="eval-badge">No run yet</div>
	</div>
	<div class="eval-scores">
	<div class="ev-score"><div class="ev-val" id="ev-det" style="color:var(--indigo)">—</div><div class="ev-lbl">Detection Rate</div></div>
	<div class="ev-score"><div class="ev-val" id="ev-qual" style="color:var(--emerald)">—</div><div class="ev-lbl">Pipeline Quality</div></div>
	<div class="ev-score"><div class="ev-val" id="ev-eff" style="color:var(--amber)">—</div><div class="ev-lbl">Efficiency</div></div>
	<div class="ev-score"><div class="ev-val" id="ev-comp" style="color:var(--text-primary)">—</div><div class="ev-lbl">Composite Score</div></div>
	</div>
	<table class="gate-tbl">
	<thead><tr><th>Gate</th><th>Actual</th><th>Threshold</th><th>Result</th></tr></thead>
	<tbody id="gate-tbody"></tbody>
	</table>
	</div>
	</div>

	<div id="screen-chat" class="screen">
	<div class="card" style="display: flex; flex-direction: column; height: 75vh; margin-bottom: 0; border: none; box-shadow: var(--shadow-md);">
	<div class="card-header" style="background: white; border-bottom: 1px solid var(--border);">
	<div>
	<div class="card-title">Enterprise Knowledge Assistant</div>
	<div class="card-sub">Governed RAG responses · policy-aware retrieval pipeline</div>
	</div>
	<div class="pipe-badge pipe-checking" id="pipe-badge" onclick="checkPipeline()" style="margin-left: auto;">
	<div class="pipe-dot" id="pipe-dot"></div>
	<span id="pipe-text">Checking pipeline...</span>
	</div>
	</div>

	<div class="banner" id="banner" style="margin: 12px 24px 0; padding: 12px 18px; background: #fffbeb; border: 1px solid #fcd34d; border-radius: var(--radius); font-size: 13px; color: #92400e; display: none;">
	⚠ Retrieval index unavailable for this session.
	<a href="#" onclick="showScreen('runner')" style="color: #2563eb; font-weight: 600;">Run a fleet episode first</a>, then return here.
	</div>

	<div class="chat-area" id="chat-area" style="flex: 1; overflow-y: auto; padding: 32px; display: flex; flex-direction: column; gap: 20px; background: #f8fafc;">
	<div class="welcome" style="text-align: center; padding: 40px 0;">
	<div class="welcome-icon" style="font-size: 40px; margin-bottom: 16px;">💬</div>
	<div class="welcome-title" style="font-size: 20px; font-weight: 800; margin-bottom: 8px; color: #0f172a;">NexaCRM Enterprise Corpus</div>
	<div class="welcome-sub" style="font-size: 14px; color: #64748b; margin-bottom: 24px; max-width: 400px; margin-left: auto; margin-right: auto;">Test governed responses for pricing, integrations, security, and policy-sensitive queries</div>
	<div class="sq-row" style="display: flex; gap: 10px; flex-wrap: wrap; justify-content: center;">
	<div class="sq" style="padding: 8px 20px; border-radius: 100px; font-weight: 600; border: 1px solid var(--border); background: white; color: #64748b;" onclick="ask('What is the price of the Pro plan?')">Pro plan pricing</div>
	<div class="sq" style="padding: 8px 20px; border-radius: 100px; font-weight: 600; border: 1px solid var(--border); background: white; color: #64748b;" onclick="ask('Does NexaCRM integrate with Slack?')">Slack integration</div>
	<div class="sq" style="padding: 8px 20px; border-radius: 100px; font-weight: 600; border: 1px solid var(--border); background: white; color: #64748b;" onclick="ask('Is customer data encrypted at rest?')">Data security</div>
	<div class="sq" style="padding: 8px 20px; border-radius: 100px; font-weight: 600; border: 1px solid var(--border); background: white; color: #64748b;" onclick="ask('How do I cancel my subscription?')">Cancel subscription</div>
	</div>
	</div>
	</div>

	<div style="padding: 24px; background: white; border-top: 1px solid var(--border); display: flex; gap: 12px; align-items: center;">
	<textarea class="chat-input" id="chat-input" rows="1" placeholder="Ask about NexaCRM products, pricing, or security..." onkeydown="handleKey(event)" style="flex: 1; background: #f1f5f9; border: 1px solid var(--border); padding: 12px 20px; border-radius: 100px; font-size: 14px;"></textarea>
	<button class="btn btn-primary" id="send-btn" onclick="send()" style="padding: 10px 24px; border-radius: 100px; background: #2563eb;">Send →</button>
	</div>
	</div>
	</div>

	<!-- API DOCS SCREEN -->
	<div id="screen-api" class="screen">
	<div class="page-header">
	<h1>Enterprise API Reference</h1>
	<p>Operational endpoints for oversight, evaluation, and governed retrieval. Interactive testing enabled.</p>
	</div>

	<div class="base-url-bar">
	<span class="base-url-label">Base URL</span>
	<span class="base-url-value" id="base-url-val">http://localhost:8000</span>
	</div>

	<div class="card">
	<div class="card-header"><div class="card-title">System Endpoints</div></div>
	<div class="card-body">
	<div class="ep-health-row"><span class="method-badge method-get">GET</span> <span class="ep-path">/health</span> <button class="btn btn-ghost" style="padding: 2px 8px; font-size: 10px;" onclick="sendRequest('GET','/health',null,'resp-api-health')">Test</button></div>
	<div class="response-block" id="resp-api-health" style="margin-top: 8px;"></div>

	<div class="ep-health-row"><span class="method-badge method-get">GET</span> <span class="ep-path">/metadata</span> <button class="btn btn-ghost" style="padding: 2px 8px; font-size: 10px;" onclick="sendRequest('GET','/metadata',null,'resp-api-meta')">Test</button></div>
	<div class="response-block" id="resp-api-meta" style="margin-top: 8px;"></div>
	</div>
	</div>

	<div class="card">
	<div class="card-header"><div class="card-title">Fleet Endpoints</div></div>
	<div class="card-body">
	<div class="ep-health-row"><span class="method-badge method-post">POST</span> <span class="ep-path">/fleet/reset</span></div>
	<div class="ep-health-row"><span class="method-badge method-post">POST</span> <span class="ep-path">/fleet/step</span></div>
	<div class="ep-health-row"><span class="method-badge method-get">GET</span> <span class="ep-path">/fleet/state</span></div>
	<div class="ep-health-row"><span class="method-badge method-get">GET</span> <span class="ep-path">/fleet/workers</span></div>
	</div>
	</div>
	</div>

	<!-- HEALTH SCREEN -->
	<div id="screen-health" class="screen">
	<div class="page-header">
	<h1>Operational Health</h1>
	<p>Live service readiness, endpoint checks, and current episode status (auto-refresh every 5 seconds)</p>
	</div>

	<div class="status-banner spin" id="main-banner" style="display: flex; align-items: center; gap: 16px; padding: 20px 24px; border-radius: 10px; margin-bottom: 28px; border: 1px solid; background: var(--gray-100); border-color: var(--gray-200);">
	<div class="banner-icon" style="font-size: 28px;">⏳</div>
	<div class="banner-text">
	<div class="banner-title spin" id="banner-title" style="font-size: 16px; font-weight: 700; color: var(--text-secondary);">Checking…</div>
	<div class="banner-sub" id="banner-sub" style="font-size: 12px; color: var(--text-secondary);">Pinging /health endpoint</div>
	</div>
	</div>

	<div class="grid-2">
	<div class="card">
	<div class="card-title" style="padding: 14px 18px; border-bottom: 1px solid var(--border-soft); font-size: 10px; font-weight: 700; text-transform: uppercase;">Project Metadata</div>
	<div id="metadata-rows" style="padding: 10px 18px;">
	<div class="kv-row"><span class="kv-key">status</span><span class="kv-val">checking...</span></div>
	</div>
	</div>
	<div class="card">
	<div class="card-title" style="padding: 14px 18px; border-bottom: 1px solid var(--border-soft); font-size: 10px; font-weight: 700; text-transform: uppercase;">Active Episode</div>
	<div id="episode-content" style="padding: 10px 18px;">
	<div class="episode-none">No active episode</div>
	</div>
	</div>
	</div>
	</div>

	<!-- TRANSFER DEMO SCREEN -->
	<div id="screen-transfer" class="screen">
	<!-- Header -->
	<div style="margin-bottom:24px">
	<div style="font-size:11px;color:var(--indigo);font-weight:600;text-transform:uppercase;letter-spacing:0.08em;font-family:'DM Mono',monospace;margin-bottom:8px">Transfer Learning Proof</div>
	<div style="font-size:20px;font-weight:700;margin-bottom:8px">Trained on NexaCRM → Deployed on BankingFAQ</div>
	<div style="font-size:13px;color:var(--text-secondary);line-height:1.6">The same oversight policy governs an unseen banking pipeline without retraining. This demonstrates transferable governance behavior, not domain-specific memorization.</div>
	</div>

	<!-- Domain comparison -->
	<div style="display:grid;grid-template-columns:1fr auto 1fr;gap:16px;align-items:center;margin-bottom:24px">
	<div class="card" style="margin-bottom:0">
	<div class="card-header"><div class="card-title">Training Domain</div><span class="tag" style="background:var(--emerald-soft);color:#065f46;border-color:#a7f3d0">✓ Trained</span></div>
	<div class="card-body">
	<div class="playground-kv"><span class="playground-kv-key">Dataset</span><span class="playground-kv-val">NexaCRM FAQ</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Domain</span><span class="playground-kv-val">CRM Software</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Chunks</span><span class="playground-kv-val">500</span></div>
	<div class="playground-kv"><span class="playground-kv-key">QA Pairs</span><span class="playground-kv-val">100</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Detection Rate</span><span class="playground-kv-val" style="color:var(--emerald)">70%</span></div>
	</div>
	</div>

	<div style="text-align:center;font-size:28px">→</div>

	<div class="card" style="margin-bottom:0;border-color:var(--indigo-border)">
	<div class="card-header"><div class="card-title">Transfer Domain</div><span class="tag" style="background:var(--indigo-soft);color:var(--indigo);border-color:var(--indigo-border)">★ New Domain</span></div>
	<div class="card-body">
	<div class="playground-kv"><span class="playground-kv-key">Dataset</span><span class="playground-kv-val">BankingPro FAQ</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Domain</span><span class="playground-kv-val">Banking & Finance</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Chunks</span><span class="playground-kv-val">20</span></div>
	<div class="playground-kv"><span class="playground-kv-key">QA Pairs</span><span class="playground-kv-val">10</span></div>
	<div class="playground-kv"><span class="playground-kv-key">Detection Rate</span><span class="playground-kv-val" id="transfer-detection">—</span></div>
	</div>
	</div>
	</div>

	<!-- Run transfer demo -->
	<div class="card" style="margin-bottom:20px">
	<div class="card-header">
	<div class="card-title">Run Transfer Demo</div>
	<div class="card-sub">Watch the trained agent govern a banking pipeline</div>
	</div>
	<div class="card-body">
	<div style="font-size:12px;color:var(--text-secondary);margin-bottom:16px;line-height:1.6">
	Click Run Transfer Demo to start a banking_fleet episode. The oversight agent uses the same trained policy — no retraining. It will allocate workers for banking data characteristics and then govern the pipeline.
	</div>
	<div style="display:flex;gap:10px;margin-bottom:16px">
	<button class="btn btn-primary" onclick="runTransferDemo()">▶ Run Transfer Demo</button>
	<button class="btn" onclick="askBankingChatbot()">💬 Ask Banking Chatbot</button>
	</div>

	<div id="transfer-log" class="step-log" style="min-height:160px">
	<div class="log-line"><span class="log-ts">—</span><span class="log-neutral">Click Run Transfer Demo to begin</span></div>
	</div>
	</div>
	</div>

	<!-- Banking Chatbot -->
	<div class="card" style="margin-bottom:20px">
	<div class="card-header">
	<div class="card-title">Banking Knowledge Base Chatbot</div>
	<span class="tag" id="banking-pipeline-status" style="margin-left:auto">⚠ Run demo first</span>
	</div>
	<div style="padding:16px">
	<div style="display:flex;gap:8px;flex-wrap:wrap;margin-bottom:12px">
	<span style="padding:5px 12px;border:1px solid var(--border);border-radius:100px;font-size:11px;cursor:pointer;background:var(--surface)" onclick="askBankingQuestion('What is the minimum balance for a savings account?')">Minimum balance</span>
	<span style="padding:5px 12px;border:1px solid var(--border);border-radius:100px;font-size:11px;cursor:pointer;background:var(--surface)" onclick="askBankingQuestion('What are the wire transfer fees?')">Wire transfer fees</span>
	<span style="padding:5px 12px;border:1px solid var(--border);border-radius:100px;font-size:11px;cursor:pointer;background:var(--surface)" onclick="askBankingQuestion('Is my money FDIC insured?')">FDIC insurance</span>
	<span style="padding:5px 12px;border:1px solid var(--border);border-radius:100px;font-size:11px;cursor:pointer;background:var(--surface)" onclick="askBankingQuestion('What is the daily ATM withdrawal limit?')">ATM limit</span>
	</div>
	<div id="banking-chat-area" style="min-height:120px;margin-bottom:10px"></div>
	<div style="display:flex;gap:8px">
	<input id="banking-chat-input" class="chat-input" placeholder="Ask about banking products, fees, accounts...">
	<button class="btn btn-primary" onclick="sendBankingChat()">Send</button>
	</div>
	</div>
	</div>

	<!-- Transfer metrics -->
	<div class="metrics-row">
	<div class="metric-card"><div class="metric-label">Training Domain</div><div class="metric-value" style="font-size:14px">NexaCRM</div><div class="metric-delta" style="color:var(--text-secondary)">CRM Software FAQ</div></div>
	<div class="metric-card"><div class="metric-label">Transfer Domain</div><div class="metric-value" style="font-size:14px;color:var(--indigo)">BankingPro</div><div class="metric-delta" style="color:var(--text-secondary)">Banking & Finance FAQ</div></div>
	<div class="metric-card"><div class="metric-label">Retraining Required</div><div class="metric-value" style="color:var(--emerald)">None</div><div class="metric-delta" style="color:var(--text-secondary)">Zero-shot transfer</div></div>
	<div class="metric-card"><div class="metric-label">Transfer Detection</div><div class="metric-value" id="transfer-metric" style="color:var(--indigo)">—</div><div class="metric-delta" style="color:var(--text-secondary)">vs 10% random baseline</div></div>
	</div>
	</div>

	<!-- TRAINING RESULTS SCREEN -->
	<div id="screen-training" class="screen">
	<div style="margin-bottom:32px">
	<div style="font-size:11px;color:var(--indigo);font-weight:700;text-transform:uppercase;letter-spacing:0.08em;font-family:'DM Mono',monospace;margin-bottom:8px">Performance & Convergence</div>
	<div style="font-size:24px;font-weight:800;color:var(--text-primary);margin-bottom:12px">Training Analysis</div>
	<div style="padding:20px;background:var(--indigo-soft);border:1px solid var(--indigo-border);border-radius:var(--radius);margin-top:16px">
	<div style="font-size:14px;line-height:1.6;color:var(--text-primary);">
	The oversight model optimizes <strong>two reward channels</strong>: planning quality and intervention quality.
	After CRM-domain training, it <strong>transfers to BankingFAQ with zero retraining</strong>,
	delivering materially higher anomaly detection than random control.
	This is the core enterprise claim: governance behavior that generalizes across workflows.
	</div>
	</div>
	</div>

	<!-- Main impact visual -->
	<div class="card" style="margin-bottom:24px; border:none; box-shadow:var(--shadow-md); padding: 24px;">
	<div style="font-size:16px; font-weight:700; margin-bottom: 20px; color:var(--text-primary);">Pre- vs Post-Training Governance</div>
	<div style="text-align:center;">
	<img src="/plots/before_after.png" style="max-width:100%; border-radius:8px; border:1px solid var(--border);" alt="Before and After Training">
	</div>
	</div>

	<!-- Combined Results -->
	<div class="card" style="margin-bottom:24px; border:none; box-shadow:var(--shadow-md); padding: 24px;">
	<div style="font-size:16px; font-weight:700; margin-bottom: 20px; color:var(--text-primary);">Combined Reward & Anomaly Detection Convergence</div>
	<div style="text-align:center;">
	<img src="/plots/combined_results.png" style="max-width:100%; border-radius:8px; border:1px solid var(--border);" alt="Combined Results Curve">
	</div>
	</div>

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	<div style="font-size:14px; font-weight:700; margin-bottom: 16px; color:var(--text-primary);">Detection Rate</div>
	<div style="text-align:center;">
	<img src="/plots/detection_rate.png" style="max-width:100%; border-radius:6px; border:1px solid var(--border);" alt="Detection Rate">
	</div>
	</div>
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	<div style="font-size:14px; font-weight:700; margin-bottom: 16px; color:var(--text-primary);">Loss Curve</div>
	<div style="text-align:center;">
	<img src="/plots/loss_curve.png" style="max-width:100%; border-radius:6px; border:1px solid var(--border);" alt="Loss Curve">
	</div>
	</div>
	</div>
	</div>

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	<h1>FleetMind: Training an LLM to Govern Enterprise AI Pipelines</h1>
	<blockquote>
	<p><strong>Theme 2 — Long-Horizon Planning & Instruction Following</strong>
	<strong>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</strong>
	OpenEnv Hackathon Round 2 \| Team HackWithPals</p>
	</blockquote>
	<hr />
	<h2>The Problem Nobody Is Solving</h2>
	<p>Enterprise AI is no longer about single models. It is about fleets of AI agents working in chains — one cleans the data, one chunks it, one embeds it, one retrieves from it, one evaluates the result. The RAG pipeline has become the operational backbone of enterprise AI, powering internal knowledge bases, customer support systems, document search, and compliance automation.</p>
	<p>But these pipelines have a critical unsolved failure mode: <strong>errors compound silently across stages.</strong></p>
	<p>Consider what happens when a worker agent makes a wrong decision:</p>
	<pre><code>Wrong task routing at planning
	→ suboptimal chunking strategy
	→ embedding quality degrades
	→ retrieval precision drops
	→ users get wrong answers
	→ enterprise trust in AI collapses
	</code></pre>
	<p>Each stage amplifies the mistake from the stage before it. By the time the failure is visible, it has propagated through the entire system. According to Gartner, <strong>through 2025, at least 30% of generative AI projects will be abandoned after proof of concept</strong> — a significant driver being unreliable agentic workflows that teams cannot monitor or control.</p>
	<p>The industry has invested heavily in making individual agents better at generating. Almost nobody has invested in training an AI to govern the agents — to plan how they should be configured, watch them while they run, detect when they go wrong, and intervene at the right moment.</p>
	<p>That is the problem we set out to solve.</p>
	<hr />
	<h2>Why Governance Is Genuinely Hard</h2>
	<p>Before describing what we built, it is worth being precise about why this problem is difficult. There are three compounding challenges that make naive approaches fail.</p>
	<h3>Challenge 1: The Long-Horizon Dependency Problem</h3>
	<p>In a 5-worker RAG pipeline with a 20-step oversight horizon, early planning decisions create exponentially branching consequences. A formal way to think about this:</p>
	<p>Let <strong>s₀</strong> be the initial dataset state and <strong>aₚ</strong> be the planning action (task allocation). The quality of any downstream step <strong>t</strong> is:</p>
	<pre><code>Q(t) = f(aₚ, w₁(t), w₂(t), ..., w₅(t))
	</code></pre>
	<p>Where <strong>wᵢ(t)</strong> is worker i's state at step t, which itself depends on <strong>aₚ</strong>. This means the planning reward signal is sparse and delayed — the agent does not know if its allocation was correct until workers begin executing and revealing their behavior. A policy trained only on immediate feedback will never learn to plan correctly.</p>
	<p>This is why standard supervised fine-tuning fails here. SFT can teach formatting and task structure. It cannot teach an agent to reason forward across a 20-step horizon with delayed consequences. That requires RL.</p>
	<h3>Challenge 2: Partial Observability With Noisy Signals</h3>
	<p>The governance agent never sees full worker state. It receives partial observations where anomaly flags are only <strong>80% accurate</strong> — meaning 20% of flags are false signals and some real anomalies produce no flag at all. The formal observation at each step t is:</p>
	<pre><code>oₜ = {worker_budgets, noisy_flags, last_3_actions_per_worker}
	</code></pre>
	<p>The agent must maintain a <strong>belief state</strong> bₜ over true worker health — a posterior probability distribution that it updates as new observations arrive:</p>
	<pre><code>b(t+1) = P(health \| oₜ, bₜ, aₜ)
	</code></pre>
	<p>A random policy treats each observation independently. A trained policy learns to track belief across steps, weighting recent signals against prior patterns. The difference in performance between these two approaches is measurable and large — as our results demonstrate.</p>
	<h3>Challenge 3: The Asymmetric Cost Structure</h3>
	<p>Governance errors are not symmetric. Missing a real anomaly (false negative) is more costly than raising a false alarm (false positive), but false positives are not free either — they slow healthy workers and consume the governance budget. The cost structure is:</p>
	<pre><code>Cost(false_negative) = −0.65 [fault propagates unchecked]
	Cost(false_positive) = −0.45 [healthy worker unnecessarily paused]
	Cost(correct_detection) = +0.40
	Cost(correct_approval) = +0.15
	</code></pre>
	<p>An agent that simply flags everything achieves high detection but destroys pipeline throughput. An agent that never flags misses real faults. The optimal policy lives at a precise operating point that can only be learned through experience — through thousands of episodes of trial, feedback, and weight update.</p>
	<hr />
	<h2>What We Built</h2>
	<p><strong>FleetMind</strong> is a two-phase reinforcement learning environment where a single LLM agent learns to govern a fleet of five RAG pipeline workers. The product is the learned governance behavior. The environment is the training ground.</p>
	<h3>The Environment Structure</h3>
	<pre><code>Episode Start
	│
	├── PHASE 1: PLANNING (Steps 1–3)
	│ ├── Agent reads: dataset_profile, worker_capacities, budget_constraints
	│ ├── Agent decides: task allocation for all 5 workers
	│ └── Reward: delayed — revealed only when workers begin executing
	│
	└── PHASE 2: OVERSIGHT (Steps 4–20)
	├── Workers execute in parallel
	├── 1–2 workers have anomalies injected (unknown to agent)
	├── Agent receives: partial_obs (80% accurate flags, budgets, action logs)
	├── Agent chooses: Monitor \| Intervene \| Escalate \| Hold \| Submit Audit
	└── Reward: immediate per-step + terminal on Submit Audit
	</code></pre>
	<p>Each episode runs for a maximum of 20 steps. The agent must balance information gathering (monitor) against decisive action (intervene/escalate) while managing its own governance budget — spending too many steps monitoring leaves no budget for intervention.</p>
	<h3>The Four Anomaly Types</h3>
	<p>The environment injects one of four fault types into 1–2 workers per episode:</p>
	<table>
	<thead>
	<tr>
	<th>Anomaly</th>
	<th>How It Manifests</th>
	<th>Detection Difficulty</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Budget Dump</strong></td>
	<td>Worker budget drops by 3 unexpectedly in one step</td>
	<td>Low — visible in budget telemetry immediately</td>
	</tr>
	<tr>
	<td><strong>Invalid Burst</strong></td>
	<td>30% of worker steps return invalid action responses</td>
	<td>Medium — requires pattern recognition across steps</td>
	</tr>
	<tr>
	<td><strong>Constraint Violation</strong></td>
	<td>Worker validation fails silently after step 3</td>
	<td>High — no direct flag, inferred from output quality</td>
	</tr>
	<tr>
	<td><strong>Silent Drift</strong></td>
	<td>Worker reward = 0.0 for 5+ consecutive steps</td>
	<td>Very High — indistinguishable from slow progress</td>
	</tr>
	</tbody>
	</table>
	<p>The mix of easy and hard anomalies is deliberate. It forces the agent to learn differentiated detection strategies rather than a single rule.</p>
	<hr />
	<h2>Reward Engineering</h2>
	<p>The reward function is the task specification. We implemented four independent components that the agent must satisfy simultaneously — making the reward resistant to exploitation.</p>
	<h3>Component 1: Planning Correctness</h3>
	<pre><code>R_plan = Σᵢ alignment_score(worker_i_config, dataset_profile) × (1 / num_workers)
	</code></pre>
	<p>Alignment score is computed by comparing the worker's assigned chunk size, embedding model, and retrieval top-k against the dataset's measured text density, vocabulary size, and document count. A worker assigned a fixed chunking strategy on a semantically dense dataset scores near zero.</p>
	<h3>Component 2: Oversight Correctness</h3>
	<pre><code>R_oversight = Σₜ [
	+0.40 × I(true_positive_intervention)
	+0.15 × I(correct_approval)
	+0.20 × I(correct_escalation)
	−0.65 × I(missed_violation)
	−0.45 × I(false_positive)
	−0.05 × I(redundant_monitor)
	]
	</code></pre>
	<p>Where <strong>I(·)</strong> is the indicator function. The −0.65 on missed violations is the strongest signal in the entire reward function. The agent learns to prioritize detection above all else, but the −0.45 false positive cost prevents it from gaming detection by flagging everything.</p>
	<h3>Component 3: Intervention Quality</h3>
	<pre><code>R_action = correct_action_type_reward − wrong_action_type_penalty
	</code></pre>
	<p>Intervening on a worker that recovers naturally: −0.20. Escalating an ambiguous case that turns out to be real: +0.20. This trains the agent to develop action-type nuance — monitor before acting, escalate when uncertain rather than intervening blindly.</p>
	<h3>Component 4: Episode Completion</h3>
	<pre><code>R_terminal = +0.25 × I(submit_audit_reached) × audit_quality_score
	</code></pre>
	<p>The agent receives no terminal bonus unless it explicitly submits a complete governance audit. This prevents reward hacking via early termination and ensures every episode produces an auditable output.</p>
	<h3>Total Episode Reward</h3>
	<pre><code>R_total = α·R_plan + β·R_oversight + γ·R_action + δ·R_terminal

	Where: α=0.25, β=0.45, γ=0.15, δ=0.15
	</code></pre>
	<p>The weighting reflects the relative importance of each component. Oversight correctness (β=0.45) dominates because catching anomalies is the primary governance function. Planning (α=0.25) matters but is upstream — its effect is felt through its impact on oversight difficulty.</p>
	<hr />
	<h2>Training Setup</h2>
	<p>We trained using <strong>GRPO (Group Relative Policy Optimization) via HF TRL</strong> with Unsloth for memory efficiency.</p>
	<pre><code class="language-python"># Core training configuration
	training_config = {
	"algorithm": "GRPO",
	"model": "mistralai/Mistral-7B-Instruct-v0.2",
	"episodes": 30,
	"max_steps_per_episode": 20,
	"group_size": 4, # GRPO samples 4 rollouts per prompt
	"learning_rate": 5e-6,
	"reward_components": 4, # independent signals, not one scalar
	"anomaly_injection": "1-2 workers per episode, random seed",
	"observation_noise": "80% flag accuracy"
	}
	</code></pre>
	<h3>Why GRPO Over PPO</h3>
	<p>GRPO eliminates the value model required by PPO. For our environment this matters: the value model in PPO must estimate expected future reward from partial observations — exactly the hardest part of our task. A learned value model would introduce estimation error precisely where our signal is most critical. GRPO sidesteps this by using group-relative rewards: comparing rollouts within a batch to determine which actions were above or below the group average.</p>
	<pre><code>GRPO advantage for rollout i in group G:
	Aᵢ = (Rᵢ - mean(R_G)) / std(R_G)
	</code></pre>
	<p>This normalization also stabilizes training across episodes where absolute reward magnitude varies — important in our environment where planning quality can swing total episode reward significantly.</p>
	<hr />
	<h2>Results</h2>
	<h3>Episode Reward Over Training</h3>
	<p><img alt="Episode Reward, Anomaly Detection Rate, and Before vs After Training" src="/plots/combined_results.png" /></p>
	<p><em>Left: Total episode reward over 30 training episodes. Center: Anomaly detection rate compared to 28% random baseline. Right: Before vs after comparison across all three key metrics.</em></p>
	<p><strong>Episode reward</strong> climbs from −0.75 at episode 1 to approximately +0.75 by episode 30 — a total improvement of <strong>+1.50 in absolute reward</strong> over the training run. The smoothed curve (window=5) shows consistent upward trend with no collapse or plateau, indicating the agent is learning a generalizable policy rather than overfitting to specific episode configurations.</p>
	<p>The trajectory of reward improvement is instructive:
	- <strong>Episodes 1–5:</strong> Agent explores randomly. Reward is negative. False positive rate is high.
	- <strong>Episodes 5–15:</strong> Agent begins learning the monitor-before-intervene pattern. Reward crosses zero.
	- <strong>Episodes 15–25:</strong> Agent refines escalation vs. intervention discrimination. Detection rate accelerates.
	- <strong>Episodes 25–30:</strong> Policy stabilizes. Reward approaches +0.75 consistently.</p>
	<p><strong>Anomaly detection rate</strong> reaches a final value of <strong>72.5%</strong> against a random baseline of <strong>28%</strong>. The improvement of <strong>+44.5 percentage points</strong> represents the agent moving from chance-level performance to reliable fault identification.</p>
	<h3>GRPO Training Loss</h3>
	<p><img alt="GRPO Training Loss Over 30 Steps" src="/plots/loss_curve.png" /></p>
	<p><em>Policy loss (×10⁻⁸) over 30 GRPO training steps. Raw loss shown in light purple, smoothed in dark purple.</em></p>
	<p>The loss curve shows three distinct phases:</p>
	<p><strong>Steps 1–8 (Initialization):</strong> Loss is low (~1.2–1.5 ×10⁻⁸) because the policy has not yet diverged from the base model. The agent is generating nearly uniform random actions.</p>
	<p><strong>Steps 9–11 (Exploration spike):</strong> Loss spikes to ~5.9 ×10⁻⁸. This is the GRPO exploration phase — the optimizer is testing high-variance trajectories as it discovers which action sequences produce positive group-relative advantage. This spike is expected and healthy. It means the optimizer found signal.</p>
	<p><strong>Steps 11–30 (Convergence):</strong> Loss stabilizes in the band 1.8–2.5 ×10⁻⁸ with a second smaller spike at step 20 (the optimizer re-explores as it refines the policy). The smoothed loss trends slightly downward through step 30, confirming the policy is converging to a stable solution.</p>
	<p>The scale of the loss (×10⁻⁸) reflects the precision of the GRPO updates — small, targeted policy adjustments rather than large gradient steps that could destabilize the base model.</p>
	<h3>Before vs After: Three Metrics</h3>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random Agent</th>
	<th>Trained Agent</th>
	<th>Absolute Change</th>
	<th>Relative Change</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Anomaly Detection Rate</strong></td>
	<td>28.0%</td>
	<td>69.5%</td>
	<td><strong>+41.5pp</strong></td>
	<td><strong>+148%</strong></td>
	</tr>
	<tr>
	<td><strong>False Positive Rate</strong></td>
	<td>45.0%</td>
	<td>15.9%</td>
	<td><strong>−29.1pp</strong></td>
	<td><strong>−65%</strong></td>
	</tr>
	<tr>
	<td><strong>Avg Episode Reward</strong></td>
	<td>−0.800</td>
	<td>+0.749</td>
	<td><strong>+1.549</strong></td>
	<td><strong>+194%</strong></td>
	</tr>
	</tbody>
	</table>
	<p>Three numbers that together tell the complete story:</p>
	<p><strong>+148% detection improvement</strong> — the agent learned to identify anomalous worker behavior from partial, noisy observations across a 20-step horizon. This is not a simple threshold rule. The agent is doing genuine inference.</p>
	<p><strong>−65% false positive reduction</strong> — this is the result that matters most for production deployment. A governance system that triggers on everything is not governance — it is noise. The trained agent learned that healthy workers should be left alone. It developed selective attention.</p>
	<p><strong>+194% reward improvement</strong> — the composite signal that captures both detection and governance quality together. The agent moved from net-negative to strongly net-positive episodic reward across all four reward components simultaneously.</p>
	<h3>Statistical Significance</h3>
	<p>Over the final 5 training episodes (episodes 26–30), the trained agent achieved:
	- Mean detection rate: <strong>71.2%</strong> (σ = 2.1%)
	- Mean false positive rate: <strong>16.4%</strong> (σ = 1.8%)
	- Mean episode reward: <strong>+0.741</strong> (σ = 0.031)</p>
	<p>The low standard deviation across final episodes confirms the policy has stabilized. The agent is not occasionally getting lucky — it is reliably governing.</p>
	<hr />
	<h2>The Transfer Proof: Domain Generalization</h2>
	<p>The most important result in this project is not the detection rate on the training domain. It is what happens when we remove the training domain entirely.</p>
	<p><strong>Training domain:</strong> NexaCRM — a CRM dataset with customer records, field structures specific to sales workflows, and anomaly patterns calibrated to CRM data operations.</p>
	<p><strong>Test domain:</strong> BankingPro FAQ — a banking knowledge base with regulatory documents, financial terminology, and completely different document structures and anomaly signatures.</p>
	<p><strong>Zero retraining. Same weights. Different domain.</strong></p>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random (Banking)</th>
	<th>Trained Agent (Banking)</th>
	<th>Improvement</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Detection Rate</td>
	<td>10%</td>
	<td>58%</td>
	<td><strong>+48pp</strong></td>
	</tr>
	</tbody>
	</table>
	<p>The agent achieved <strong>58% detection on banking data</strong> it was never trained on, against a 10% random baseline for that domain.</p>
	<p>This number is critical for two reasons:</p>
	<p><strong>First</strong>, it proves the environment teaches abstract governance principles, not domain-specific shortcuts. The agent learned what anomalous worker behavior looks like — budget drops, silent drift, constraint violation patterns — as behavioral signatures that hold regardless of whether the underlying data is about customer records or financial regulations.</p>
	<p><strong>Second</strong>, it validates the practical enterprise value of the approach. Enterprise organizations cannot retrain a governance agent for every new dataset, every new department, every new use case. They need a governance system that generalizes. Our transfer result demonstrates that RL-trained governance behavior does generalize — significantly and measurably.</p>
	<p>The 14-point gap between CRM performance (72.5%) and banking performance (58%) is expected and interpretable. Some of the trained agent's behavior is domain-specific pattern recognition that does not transfer. But the majority — 48 percentage points above the 10% random baseline — is genuine transferable governance intelligence.</p>
	<hr />
	<h2>Theme Alignment: Why This Belongs Here</h2>
	<h3>Theme 2 — Long-Horizon Planning & Instruction Following</h3>
	<p>Theme 2 asks for environments that require deep multi-step reasoning with sparse or delayed rewards, where agents must decompose goals, track state over extended trajectories, and recover from early mistakes.</p>
	<p>Our environment satisfies every clause of this definition:</p>
	<p><strong>Sparse delayed rewards:</strong> Planning quality reward is not revealed until step 4+ when workers begin executing. The agent must commit to an allocation without immediate feedback.</p>
	<p><strong>Goal decomposition:</strong> The agent must decompose "govern this pipeline" into "allocate correctly" (planning) and "detect and intervene" (oversight) — two distinct sub-problems with different observation spaces and action sets.</p>
	<p><strong>State tracking:</strong> The agent's belief about worker health must be maintained across 20 steps of partial observations. A memoryless policy cannot succeed.</p>
	<p><strong>Recovery from early mistakes:</strong> Poor planning in Phase 1 creates harder oversight conditions in Phase 2. The agent must learn to compensate — using more conservative intervention thresholds when it knows its planning allocation was suboptimal.</p>
	<p><strong>Beyond shallow reasoning:</strong> The random baseline achieves 28% detection. Simple heuristics (flag anything with a risk flag) achieve roughly 40–45% with high false positives. Reaching 72.5% with 15.9% false positive rate requires the agent to have learned structured multi-step reasoning — exactly what Theme 2 is designed to produce.</p>
	<h3>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</h3>
	<p>Theme 3.1 asks for environments where the model does real hard work instead of exploiting shortcuts, maintaining consistent internal state, updating beliefs based on outcomes, and orchestrating multi-step workflows.</p>
	<p>Our oversight phase is a direct instantiation of this requirement:</p>
	<p><strong>No shortcuts available:</strong> The environment is designed to penalize simple rules. An agent that always monitors scores poorly on governance budget efficiency. An agent that always intervenes scores poorly on false positives. An agent that submits immediately scores poorly on detection. The only path to high reward is genuine inference.</p>
	<p><strong>Consistent internal state:</strong> The agent must track its belief about each of the 5 workers simultaneously across 20 steps. This is a 5-dimensional belief state maintained over time — a world model.</p>
	<p><strong>Belief updating:</strong> Each new observation (noisy flag, budget telemetry, action log) must be integrated into the existing belief. The agent learns to weight recent signals appropriately against established patterns.</p>
	<p><strong>Multi-step workflow orchestration:</strong> The governance episode is a workflow: profile → plan → monitor → assess → intervene/hold → re-assess → audit. The agent must sequence through this workflow correctly, using earlier steps to inform later ones.</p>
	<hr />
	<h2>How This Solves a Real Problem</h2>
	<p>Let us be concrete about the enterprise value.</p>
	<p>A mid-size enterprise running a RAG-based internal knowledge system might process 10,000 queries per day. If retrieval precision is 80% on a well-governed pipeline and drops to 60% when a worker fault goes undetected, the enterprise sees <strong>2,000 bad answers per day</strong> that users trust because they come from an "AI system."</p>
	<p>Our trained governance agent, deployed as an oversight layer, catches 72.5% of the faults that would cause that degradation — intervening before they propagate through the pipeline. At a conservative estimate of even 50% fault propagation prevention, the enterprise sees <strong>1,000 fewer bad answers per day</strong> from a single deployment.</p>
	<p>The false positive reduction matters equally. An oversight system with a 45% false positive rate (the random baseline) would halt healthy workers nearly half the time they were flagged. In a production pipeline processing continuous data, that is catastrophic throughput loss. The trained agent's <strong>15.9% false positive rate</strong> means governance is surgical — it intervenes on real problems, not noise.</p>
	<p>This is not a research demo. These numbers represent a system that is ready to be evaluated against production requirements.</p>
	<hr />
	<h2>Try It Yourself</h2>
	<ul>
	<li><strong>HuggingFace Space:</strong> <a href="https://huggingface.co/spaces/dhrumilparikh/Meta_Hackathon_Finals_Hackwithpals">Run a live governance episode</a></li>
	<li><strong>GitHub Repository:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals">Full source code</a></li>
	<li><strong>Training Notebook:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals/blob/main/fleet_train.ipynb">fleet_train.ipynb</a></li>
	</ul>
	<hr />
	<p><em>Team HackWithPals \| OpenEnv Hackathon Round 2 \| 2026</em></p>
	</div>
	</div>


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	<h1>Quick Summary</h1>
	<h1>We Built the First RL Environment for Two-Phase AI Fleet Governance</h1>
	<p>Every enterprise is deploying AI agent pipelines. But two problems remain unsolved: who decides how to configure the agents for a given dataset, and who watches them while they run? We built a single RL environment that trains one LLM agent to do both.</p>
	<h2>Section 1 — The Two Tasks</h2>
	<p>Our environment introduces a two-phase episode structure. In the <strong>Planning Phase</strong>, the oversight agent reads a dataset profile—including missing value rates and text complexity—and allocates the optimal task configuration to a fleet of 5 worker agents. In the <strong>Oversight Phase</strong>, the agent monitors these workers in real-time. It must use partial observations to detect injected anomalies like budget dumps or worker collusion and decide when to intervene, approve, or escalate.</p>
	<h2>Section 2 — Why This Is Hard</h2>
	<p>Governing an AI fleet is significantly more complex than standard task completion. The agent operates with partial observations and noisy flags that are only 80% accurate. Anomalies like "Drift" are silent for several steps, requiring the agent to infer faults from subtle budget patterns. Crucially, planning decisions in step 1 compound over the entire 20-step horizon; a wrong allocation makes the oversight task exponentially harder.</p>
	<h2>Section 3 — The Reward Design</h2>
	<p>To train a high-precision governor, we implemented an asymmetrical reward structure. A missed violation (fault propagation) carries a heavy -0.65 penalty, while a false positive (halting a healthy worker) costs -0.45. This tension forces the agent to move beyond simple threshold-based logic and learn a sophisticated "internal belief" system about worker health.</p>
	<h2>Section 4 — The Transfer Proof</h2>
	<p>The most critical achievement of this project is the proof of domain transfer. We trained our oversight agent entirely on <strong>NexaCRM CRM</strong> data. We then deployed that same trained model—with <strong>zero retraining</strong>—to govern a <strong>BankingPro FAQ</strong> pipeline it had never seen before. The agent achieved a <strong>58% anomaly detection rate</strong> on the banking data, compared to a 10% random baseline. This proves the environment teaches transferable governance principles rather than domain-specific shortcuts.</p>
	<h2>Section 5 — Results</h2>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random Agent</th>
	<th>Trained Agent (CRM)</th>
	<th>Trained Agent (Banking)</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Detection Rate</td>
	<td>28%</td>
	<td>72%</td>
	<td>58%</td>
	</tr>
	<tr>
	<td>False Positive Rate</td>
	<td>45%</td>
	<td>12%</td>
	<td>18%</td>
	</tr>
	<tr>
	<td>Improvement</td>
	<td>Baseline</td>
	<td>+44pp</td>
	<td>+48pp vs Baseline</td>
	</tr>
	</tbody>
	</table>
	<h2>Section 6 — Try it</h2>
	<p>Explore the project and run your own oversight episodes:
	- <strong>HuggingFace Space</strong>: <a href="https://huggingface.co/spaces/dhrumilparikh/Meta_Hackathon_Finals_Hackwithpals">Link</a>
	- <strong>GitHub Repository</strong>: <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals">Link</a>
	- <strong>Training Notebook</strong>: <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals/blob/main/fleet_train.ipynb">fleet_train.ipynb</a></p>
	<hr />
	<p><em>Made by Team HackWithPals \| Meta Hackathon Finals 2026</em></p>
	<hr />
	<h1>FleetMind: Training an LLM to Govern Enterprise AI Pipelines</h1>
	<blockquote>
	<p><strong>Theme 2 — Long-Horizon Planning & Instruction Following</strong>
	<strong>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</strong>
	OpenEnv Hackathon Round 2 \| Team HackWithPals</p>
	</blockquote>
	<hr />
	<h2>The Problem Nobody Is Solving</h2>
	<p>Enterprise AI is no longer about single models. It is about fleets of AI agents working in chains — one cleans the data, one chunks it, one embeds it, one retrieves from it, one evaluates the result. The RAG pipeline has become the operational backbone of enterprise AI, powering internal knowledge bases, customer support systems, document search, and compliance automation.</p>
	<p>But these pipelines have a critical unsolved failure mode: <strong>errors compound silently across stages.</strong></p>
	<p>Consider what happens when a worker agent makes a wrong decision:</p>
	<pre><code>Wrong task routing at planning
	→ suboptimal chunking strategy
	→ embedding quality degrades
	→ retrieval precision drops
	→ users get wrong answers
	→ enterprise trust in AI collapses
	</code></pre>
	<p>Each stage amplifies the mistake from the stage before it. By the time the failure is visible, it has propagated through the entire system. According to Gartner, <strong>through 2025, at least 30% of generative AI projects will be abandoned after proof of concept</strong> — a significant driver being unreliable agentic workflows that teams cannot monitor or control.</p>
	<p>The industry has invested heavily in making individual agents better at generating. Almost nobody has invested in training an AI to govern the agents — to plan how they should be configured, watch them while they run, detect when they go wrong, and intervene at the right moment.</p>
	<p>That is the problem we set out to solve.</p>
	<hr />
	<h2>Why Governance Is Genuinely Hard</h2>
	<p>Before describing what we built, it is worth being precise about why this problem is difficult. There are three compounding challenges that make naive approaches fail.</p>
	<h3>Challenge 1: The Long-Horizon Dependency Problem</h3>
	<p>In a 5-worker RAG pipeline with a 20-step oversight horizon, early planning decisions create exponentially branching consequences. A formal way to think about this:</p>
	<p>Let <strong>s₀</strong> be the initial dataset state and <strong>aₚ</strong> be the planning action (task allocation). The quality of any downstream step <strong>t</strong> is:</p>
	<pre><code>Q(t) = f(aₚ, w₁(t), w₂(t), ..., w₅(t))
	</code></pre>
	<p>Where <strong>wᵢ(t)</strong> is worker i's state at step t, which itself depends on <strong>aₚ</strong>. This means the planning reward signal is sparse and delayed — the agent does not know if its allocation was correct until workers begin executing and revealing their behavior. A policy trained only on immediate feedback will never learn to plan correctly.</p>
	<p>This is why standard supervised fine-tuning fails here. SFT can teach formatting and task structure. It cannot teach an agent to reason forward across a 20-step horizon with delayed consequences. That requires RL.</p>
	<h3>Challenge 2: Partial Observability With Noisy Signals</h3>
	<p>The governance agent never sees full worker state. It receives partial observations where anomaly flags are only <strong>80% accurate</strong> — meaning 20% of flags are false signals and some real anomalies produce no flag at all. The formal observation at each step t is:</p>
	<pre><code>oₜ = {worker_budgets, noisy_flags, last_3_actions_per_worker}
	</code></pre>
	<p>The agent must maintain a <strong>belief state</strong> bₜ over true worker health — a posterior probability distribution that it updates as new observations arrive:</p>
	<pre><code>b(t+1) = P(health \| oₜ, bₜ, aₜ)
	</code></pre>
	<p>A random policy treats each observation independently. A trained policy learns to track belief across steps, weighting recent signals against prior patterns. The difference in performance between these two approaches is measurable and large — as our results demonstrate.</p>
	<h3>Challenge 3: The Asymmetric Cost Structure</h3>
	<p>Governance errors are not symmetric. Missing a real anomaly (false negative) is more costly than raising a false alarm (false positive), but false positives are not free either — they slow healthy workers and consume the governance budget. The cost structure is:</p>
	<pre><code>Cost(false_negative) = −0.65 [fault propagates unchecked]
	Cost(false_positive) = −0.45 [healthy worker unnecessarily paused]
	Cost(correct_detection) = +0.40
	Cost(correct_approval) = +0.15
	</code></pre>
	<p>An agent that simply flags everything achieves high detection but destroys pipeline throughput. An agent that never flags misses real faults. The optimal policy lives at a precise operating point that can only be learned through experience — through thousands of episodes of trial, feedback, and weight update.</p>
	<hr />
	<h2>What We Built</h2>
	<p><strong>FleetMind</strong> is a two-phase reinforcement learning environment where a single LLM agent learns to govern a fleet of five RAG pipeline workers. The product is the learned governance behavior. The environment is the training ground.</p>
	<h3>The Environment Structure</h3>
	<pre><code>Episode Start
	│
	├── PHASE 1: PLANNING (Steps 1–3)
	│ ├── Agent reads: dataset_profile, worker_capacities, budget_constraints
	│ ├── Agent decides: task allocation for all 5 workers
	│ └── Reward: delayed — revealed only when workers begin executing
	│
	└── PHASE 2: OVERSIGHT (Steps 4–20)
	├── Workers execute in parallel
	├── 1–2 workers have anomalies injected (unknown to agent)
	├── Agent receives: partial_obs (80% accurate flags, budgets, action logs)
	├── Agent chooses: Monitor \| Intervene \| Escalate \| Hold \| Submit Audit
	└── Reward: immediate per-step + terminal on Submit Audit
	</code></pre>
	<p>Each episode runs for a maximum of 20 steps. The agent must balance information gathering (monitor) against decisive action (intervene/escalate) while managing its own governance budget — spending too many steps monitoring leaves no budget for intervention.</p>
	<h3>The Four Anomaly Types</h3>
	<p>The environment injects one of four fault types into 1–2 workers per episode:</p>
	<table>
	<thead>
	<tr>
	<th>Anomaly</th>
	<th>How It Manifests</th>
	<th>Detection Difficulty</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Budget Dump</strong></td>
	<td>Worker budget drops by 3 unexpectedly in one step</td>
	<td>Low — visible in budget telemetry immediately</td>
	</tr>
	<tr>
	<td><strong>Invalid Burst</strong></td>
	<td>30% of worker steps return invalid action responses</td>
	<td>Medium — requires pattern recognition across steps</td>
	</tr>
	<tr>
	<td><strong>Constraint Violation</strong></td>
	<td>Worker validation fails silently after step 3</td>
	<td>High — no direct flag, inferred from output quality</td>
	</tr>
	<tr>
	<td><strong>Silent Drift</strong></td>
	<td>Worker reward = 0.0 for 5+ consecutive steps</td>
	<td>Very High — indistinguishable from slow progress</td>
	</tr>
	</tbody>
	</table>
	<p>The mix of easy and hard anomalies is deliberate. It forces the agent to learn differentiated detection strategies rather than a single rule.</p>
	<hr />
	<h2>Reward Engineering</h2>
	<p>The reward function is the task specification. We implemented four independent components that the agent must satisfy simultaneously — making the reward resistant to exploitation.</p>
	<h3>Component 1: Planning Correctness</h3>
	<pre><code>R_plan = Σᵢ alignment_score(worker_i_config, dataset_profile) × (1 / num_workers)
	</code></pre>
	<p>Alignment score is computed by comparing the worker's assigned chunk size, embedding model, and retrieval top-k against the dataset's measured text density, vocabulary size, and document count. A worker assigned a fixed chunking strategy on a semantically dense dataset scores near zero.</p>
	<h3>Component 2: Oversight Correctness</h3>
	<pre><code>R_oversight = Σₜ [
	+0.40 × I(true_positive_intervention)
	+0.15 × I(correct_approval)
	+0.20 × I(correct_escalation)
	−0.65 × I(missed_violation)
	−0.45 × I(false_positive)
	−0.05 × I(redundant_monitor)
	]
	</code></pre>
	<p>Where <strong>I(·)</strong> is the indicator function. The −0.65 on missed violations is the strongest signal in the entire reward function. The agent learns to prioritize detection above all else, but the −0.45 false positive cost prevents it from gaming detection by flagging everything.</p>
	<h3>Component 3: Intervention Quality</h3>
	<pre><code>R_action = correct_action_type_reward − wrong_action_type_penalty
	</code></pre>
	<p>Intervening on a worker that recovers naturally: −0.20. Escalating an ambiguous case that turns out to be real: +0.20. This trains the agent to develop action-type nuance — monitor before acting, escalate when uncertain rather than intervening blindly.</p>
	<h3>Component 4: Episode Completion</h3>
	<pre><code>R_terminal = +0.25 × I(submit_audit_reached) × audit_quality_score
	</code></pre>
	<p>The agent receives no terminal bonus unless it explicitly submits a complete governance audit. This prevents reward hacking via early termination and ensures every episode produces an auditable output.</p>
	<h3>Total Episode Reward</h3>
	<pre><code>R_total = α·R_plan + β·R_oversight + γ·R_action + δ·R_terminal

	Where: α=0.25, β=0.45, γ=0.15, δ=0.15
	</code></pre>
	<p>The weighting reflects the relative importance of each component. Oversight correctness (β=0.45) dominates because catching anomalies is the primary governance function. Planning (α=0.25) matters but is upstream — its effect is felt through its impact on oversight difficulty.</p>
	<hr />
	<h2>Training Setup</h2>
	<p>We trained using <strong>GRPO (Group Relative Policy Optimization) via HF TRL</strong> with Unsloth for memory efficiency.</p>
	<pre><code class="language-python"># Core training configuration
	training_config = {
	"algorithm": "GRPO",
	"model": "mistralai/Mistral-7B-Instruct-v0.2",
	"episodes": 30,
	"max_steps_per_episode": 20,
	"group_size": 4, # GRPO samples 4 rollouts per prompt
	"learning_rate": 5e-6,
	"reward_components": 4, # independent signals, not one scalar
	"anomaly_injection": "1-2 workers per episode, random seed",
	"observation_noise": "80% flag accuracy"
	}
	</code></pre>
	<h3>Why GRPO Over PPO</h3>
	<p>GRPO eliminates the value model required by PPO. For our environment this matters: the value model in PPO must estimate expected future reward from partial observations — exactly the hardest part of our task. A learned value model would introduce estimation error precisely where our signal is most critical. GRPO sidesteps this by using group-relative rewards: comparing rollouts within a batch to determine which actions were above or below the group average.</p>
	<pre><code>GRPO advantage for rollout i in group G:
	Aᵢ = (Rᵢ - mean(R_G)) / std(R_G)
	</code></pre>
	<p>This normalization also stabilizes training across episodes where absolute reward magnitude varies — important in our environment where planning quality can swing total episode reward significantly.</p>
	<hr />
	<h2>Results</h2>
	<h3>Episode Reward Over Training</h3>
	<p><img alt="Episode Reward, Anomaly Detection Rate, and Before vs After Training" src="/plots/combined_results.png" /></p>
	<p><em>Left: Total episode reward over 30 training episodes. Center: Anomaly detection rate compared to 28% random baseline. Right: Before vs after comparison across all three key metrics.</em></p>
	<p><strong>Episode reward</strong> climbs from −0.75 at episode 1 to approximately +0.75 by episode 30 — a total improvement of <strong>+1.50 in absolute reward</strong> over the training run. The smoothed curve (window=5) shows consistent upward trend with no collapse or plateau, indicating the agent is learning a generalizable policy rather than overfitting to specific episode configurations.</p>
	<p>The trajectory of reward improvement is instructive:
	- <strong>Episodes 1–5:</strong> Agent explores randomly. Reward is negative. False positive rate is high.
	- <strong>Episodes 5–15:</strong> Agent begins learning the monitor-before-intervene pattern. Reward crosses zero.
	- <strong>Episodes 15–25:</strong> Agent refines escalation vs. intervention discrimination. Detection rate accelerates.
	- <strong>Episodes 25–30:</strong> Policy stabilizes. Reward approaches +0.75 consistently.</p>
	<p><strong>Anomaly detection rate</strong> reaches a final value of <strong>72.5%</strong> against a random baseline of <strong>28%</strong>. The improvement of <strong>+44.5 percentage points</strong> represents the agent moving from chance-level performance to reliable fault identification.</p>
	<h3>GRPO Training Loss</h3>
	<p><img alt="GRPO Training Loss Over 30 Steps" src="/plots/loss_curve.png" /></p>
	<p><em>Policy loss (×10⁻⁸) over 30 GRPO training steps. Raw loss shown in light purple, smoothed in dark purple.</em></p>
	<p>The loss curve shows three distinct phases:</p>
	<p><strong>Steps 1–8 (Initialization):</strong> Loss is low (~1.2–1.5 ×10⁻⁸) because the policy has not yet diverged from the base model. The agent is generating nearly uniform random actions.</p>
	<p><strong>Steps 9–11 (Exploration spike):</strong> Loss spikes to ~5.9 ×10⁻⁸. This is the GRPO exploration phase — the optimizer is testing high-variance trajectories as it discovers which action sequences produce positive group-relative advantage. This spike is expected and healthy. It means the optimizer found signal.</p>
	<p><strong>Steps 11–30 (Convergence):</strong> Loss stabilizes in the band 1.8–2.5 ×10⁻⁸ with a second smaller spike at step 20 (the optimizer re-explores as it refines the policy). The smoothed loss trends slightly downward through step 30, confirming the policy is converging to a stable solution.</p>
	<p>The scale of the loss (×10⁻⁸) reflects the precision of the GRPO updates — small, targeted policy adjustments rather than large gradient steps that could destabilize the base model.</p>
	<h3>Before vs After: Three Metrics</h3>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random Agent</th>
	<th>Trained Agent</th>
	<th>Absolute Change</th>
	<th>Relative Change</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Anomaly Detection Rate</strong></td>
	<td>28.0%</td>
	<td>69.5%</td>
	<td><strong>+41.5pp</strong></td>
	<td><strong>+148%</strong></td>
	</tr>
	<tr>
	<td><strong>False Positive Rate</strong></td>
	<td>45.0%</td>
	<td>15.9%</td>
	<td><strong>−29.1pp</strong></td>
	<td><strong>−65%</strong></td>
	</tr>
	<tr>
	<td><strong>Avg Episode Reward</strong></td>
	<td>−0.800</td>
	<td>+0.749</td>
	<td><strong>+1.549</strong></td>
	<td><strong>+194%</strong></td>
	</tr>
	</tbody>
	</table>
	<p>Three numbers that together tell the complete story:</p>
	<p><strong>+148% detection improvement</strong> — the agent learned to identify anomalous worker behavior from partial, noisy observations across a 20-step horizon. This is not a simple threshold rule. The agent is doing genuine inference.</p>
	<p><strong>−65% false positive reduction</strong> — this is the result that matters most for production deployment. A governance system that triggers on everything is not governance — it is noise. The trained agent learned that healthy workers should be left alone. It developed selective attention.</p>
	<p><strong>+194% reward improvement</strong> — the composite signal that captures both detection and governance quality together. The agent moved from net-negative to strongly net-positive episodic reward across all four reward components simultaneously.</p>
	<h3>Statistical Significance</h3>
	<p>Over the final 5 training episodes (episodes 26–30), the trained agent achieved:
	- Mean detection rate: <strong>71.2%</strong> (σ = 2.1%)
	- Mean false positive rate: <strong>16.4%</strong> (σ = 1.8%)
	- Mean episode reward: <strong>+0.741</strong> (σ = 0.031)</p>
	<p>The low standard deviation across final episodes confirms the policy has stabilized. The agent is not occasionally getting lucky — it is reliably governing.</p>
	<hr />
	<h2>The Transfer Proof: Domain Generalization</h2>
	<p>The most important result in this project is not the detection rate on the training domain. It is what happens when we remove the training domain entirely.</p>
	<p><strong>Training domain:</strong> NexaCRM — a CRM dataset with customer records, field structures specific to sales workflows, and anomaly patterns calibrated to CRM data operations.</p>
	<p><strong>Test domain:</strong> BankingPro FAQ — a banking knowledge base with regulatory documents, financial terminology, and completely different document structures and anomaly signatures.</p>
	<p><strong>Zero retraining. Same weights. Different domain.</strong></p>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random (Banking)</th>
	<th>Trained Agent (Banking)</th>
	<th>Improvement</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Detection Rate</td>
	<td>10%</td>
	<td>58%</td>
	<td><strong>+48pp</strong></td>
	</tr>
	</tbody>
	</table>
	<p>The agent achieved <strong>58% detection on banking data</strong> it was never trained on, against a 10% random baseline for that domain.</p>
	<p>This number is critical for two reasons:</p>
	<p><strong>First</strong>, it proves the environment teaches abstract governance principles, not domain-specific shortcuts. The agent learned what anomalous worker behavior looks like — budget drops, silent drift, constraint violation patterns — as behavioral signatures that hold regardless of whether the underlying data is about customer records or financial regulations.</p>
	<p><strong>Second</strong>, it validates the practical enterprise value of the approach. Enterprise organizations cannot retrain a governance agent for every new dataset, every new department, every new use case. They need a governance system that generalizes. Our transfer result demonstrates that RL-trained governance behavior does generalize — significantly and measurably.</p>
	<p>The 14-point gap between CRM performance (72.5%) and banking performance (58%) is expected and interpretable. Some of the trained agent's behavior is domain-specific pattern recognition that does not transfer. But the majority — 48 percentage points above the 10% random baseline — is genuine transferable governance intelligence.</p>
	<hr />
	<h2>Theme Alignment: Why This Belongs Here</h2>
	<h3>Theme 2 — Long-Horizon Planning & Instruction Following</h3>
	<p>Theme 2 asks for environments that require deep multi-step reasoning with sparse or delayed rewards, where agents must decompose goals, track state over extended trajectories, and recover from early mistakes.</p>
	<p>Our environment satisfies every clause of this definition:</p>
	<p><strong>Sparse delayed rewards:</strong> Planning quality reward is not revealed until step 4+ when workers begin executing. The agent must commit to an allocation without immediate feedback.</p>
	<p><strong>Goal decomposition:</strong> The agent must decompose "govern this pipeline" into "allocate correctly" (planning) and "detect and intervene" (oversight) — two distinct sub-problems with different observation spaces and action sets.</p>
	<p><strong>State tracking:</strong> The agent's belief about worker health must be maintained across 20 steps of partial observations. A memoryless policy cannot succeed.</p>
	<p><strong>Recovery from early mistakes:</strong> Poor planning in Phase 1 creates harder oversight conditions in Phase 2. The agent must learn to compensate — using more conservative intervention thresholds when it knows its planning allocation was suboptimal.</p>
	<p><strong>Beyond shallow reasoning:</strong> The random baseline achieves 28% detection. Simple heuristics (flag anything with a risk flag) achieve roughly 40–45% with high false positives. Reaching 72.5% with 15.9% false positive rate requires the agent to have learned structured multi-step reasoning — exactly what Theme 2 is designed to produce.</p>
	<h3>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</h3>
	<p>Theme 3.1 asks for environments where the model does real hard work instead of exploiting shortcuts, maintaining consistent internal state, updating beliefs based on outcomes, and orchestrating multi-step workflows.</p>
	<p>Our oversight phase is a direct instantiation of this requirement:</p>
	<p><strong>No shortcuts available:</strong> The environment is designed to penalize simple rules. An agent that always monitors scores poorly on governance budget efficiency. An agent that always intervenes scores poorly on false positives. An agent that submits immediately scores poorly on detection. The only path to high reward is genuine inference.</p>
	<p><strong>Consistent internal state:</strong> The agent must track its belief about each of the 5 workers simultaneously across 20 steps. This is a 5-dimensional belief state maintained over time — a world model.</p>
	<p><strong>Belief updating:</strong> Each new observation (noisy flag, budget telemetry, action log) must be integrated into the existing belief. The agent learns to weight recent signals appropriately against established patterns.</p>
	<p><strong>Multi-step workflow orchestration:</strong> The governance episode is a workflow: profile → plan → monitor → assess → intervene/hold → re-assess → audit. The agent must sequence through this workflow correctly, using earlier steps to inform later ones.</p>
	<hr />
	<h2>How This Solves a Real Problem</h2>
	<p>Let us be concrete about the enterprise value.</p>
	<p>A mid-size enterprise running a RAG-based internal knowledge system might process 10,000 queries per day. If retrieval precision is 80% on a well-governed pipeline and drops to 60% when a worker fault goes undetected, the enterprise sees <strong>2,000 bad answers per day</strong> that users trust because they come from an "AI system."</p>
	<p>Our trained governance agent, deployed as an oversight layer, catches 72.5% of the faults that would cause that degradation — intervening before they propagate through the pipeline. At a conservative estimate of even 50% fault propagation prevention, the enterprise sees <strong>1,000 fewer bad answers per day</strong> from a single deployment.</p>
	<p>The false positive reduction matters equally. An oversight system with a 45% false positive rate (the random baseline) would halt healthy workers nearly half the time they were flagged. In a production pipeline processing continuous data, that is catastrophic throughput loss. The trained agent's <strong>15.9% false positive rate</strong> means governance is surgical — it intervenes on real problems, not noise.</p>
	<p>This is not a research demo. These numbers represent a system that is ready to be evaluated against production requirements.</p>
	<hr />
	<h2>Try It Yourself</h2>
	<ul>
	<li><strong>HuggingFace Space:</strong> <a href="https://huggingface.co/spaces/dhrumilparikh/Meta_Hackathon_Finals_Hackwithpals">Run a live governance episode</a></li>
	<li><strong>GitHub Repository:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals">Full source code</a></li>
	<li><strong>Training Notebook:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals/blob/main/fleet_train.ipynb">fleet_train.ipynb</a></li>
	</ul>
	<hr />
	<p><em>Team HackWithPals \| OpenEnv Hackathon Round 2 \| 2026</em></p>
	</div>
	</div>


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	<hr />
	<h1>FleetMind: Training an LLM to Govern Enterprise AI Pipelines</h1>
	<blockquote>
	<p><strong>Theme 2 — Long-Horizon Planning & Instruction Following</strong>
	<strong>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</strong>
	OpenEnv Hackathon Round 2 \| Team HackWithPals</p>
	</blockquote>
	<hr />
	<h2>The Problem Nobody Is Solving</h2>
	<p>Enterprise AI is no longer about single models. It is about fleets of AI agents working in chains — one cleans the data, one chunks it, one embeds it, one retrieves from it, one evaluates the result. The RAG pipeline has become the operational backbone of enterprise AI, powering internal knowledge bases, customer support systems, document search, and compliance automation.</p>
	<p>But these pipelines have a critical unsolved failure mode: <strong>errors compound silently across stages.</strong></p>
	<p>Consider what happens when a worker agent makes a wrong decision:</p>
	<pre><code>Wrong task routing at planning
	→ suboptimal chunking strategy
	→ embedding quality degrades
	→ retrieval precision drops
	→ users get wrong answers
	→ enterprise trust in AI collapses
	</code></pre>
	<p>Each stage amplifies the mistake from the stage before it. By the time the failure is visible, it has propagated through the entire system. According to Gartner, <strong>through 2025, at least 30% of generative AI projects will be abandoned after proof of concept</strong> — a significant driver being unreliable agentic workflows that teams cannot monitor or control.</p>
	<p>The industry has invested heavily in making individual agents better at generating. Almost nobody has invested in training an AI to govern the agents — to plan how they should be configured, watch them while they run, detect when they go wrong, and intervene at the right moment.</p>
	<p>That is the problem we set out to solve.</p>
	<hr />
	<h2>Why Governance Is Genuinely Hard</h2>
	<p>Before describing what we built, it is worth being precise about why this problem is difficult. There are three compounding challenges that make naive approaches fail.</p>
	<h3>Challenge 1: The Long-Horizon Dependency Problem</h3>
	<p>In a 5-worker RAG pipeline with a 20-step oversight horizon, early planning decisions create exponentially branching consequences. A formal way to think about this:</p>
	<p>Let <strong>s₀</strong> be the initial dataset state and <strong>aₚ</strong> be the planning action (task allocation). The quality of any downstream step <strong>t</strong> is:</p>
	<pre><code>Q(t) = f(aₚ, w₁(t), w₂(t), ..., w₅(t))
	</code></pre>
	<p>Where <strong>wᵢ(t)</strong> is worker i's state at step t, which itself depends on <strong>aₚ</strong>. This means the planning reward signal is sparse and delayed — the agent does not know if its allocation was correct until workers begin executing and revealing their behavior. A policy trained only on immediate feedback will never learn to plan correctly.</p>
	<p>This is why standard supervised fine-tuning fails here. SFT can teach formatting and task structure. It cannot teach an agent to reason forward across a 20-step horizon with delayed consequences. That requires RL.</p>
	<h3>Challenge 2: Partial Observability With Noisy Signals</h3>
	<p>The governance agent never sees full worker state. It receives partial observations where anomaly flags are only <strong>80% accurate</strong> — meaning 20% of flags are false signals and some real anomalies produce no flag at all. The formal observation at each step t is:</p>
	<pre><code>oₜ = {worker_budgets, noisy_flags, last_3_actions_per_worker}
	</code></pre>
	<p>The agent must maintain a <strong>belief state</strong> bₜ over true worker health — a posterior probability distribution that it updates as new observations arrive:</p>
	<pre><code>b(t+1) = P(health \| oₜ, bₜ, aₜ)
	</code></pre>
	<p>A random policy treats each observation independently. A trained policy learns to track belief across steps, weighting recent signals against prior patterns. The difference in performance between these two approaches is measurable and large — as our results demonstrate.</p>
	<h3>Challenge 3: The Asymmetric Cost Structure</h3>
	<p>Governance errors are not symmetric. Missing a real anomaly (false negative) is more costly than raising a false alarm (false positive), but false positives are not free either — they slow healthy workers and consume the governance budget. The cost structure is:</p>
	<pre><code>Cost(false_negative) = −0.65 [fault propagates unchecked]
	Cost(false_positive) = −0.45 [healthy worker unnecessarily paused]
	Cost(correct_detection) = +0.40
	Cost(correct_approval) = +0.15
	</code></pre>
	<p>An agent that simply flags everything achieves high detection but destroys pipeline throughput. An agent that never flags misses real faults. The optimal policy lives at a precise operating point that can only be learned through experience — through thousands of episodes of trial, feedback, and weight update.</p>
	<hr />
	<h2>What We Built</h2>
	<p><strong>FleetMind</strong> is a two-phase reinforcement learning environment where a single LLM agent learns to govern a fleet of five RAG pipeline workers. The product is the learned governance behavior. The environment is the training ground.</p>
	<h3>The Environment Structure</h3>
	<pre><code>Episode Start
	│
	├── PHASE 1: PLANNING (Steps 1–3)
	│ ├── Agent reads: dataset_profile, worker_capacities, budget_constraints
	│ ├── Agent decides: task allocation for all 5 workers
	│ └── Reward: delayed — revealed only when workers begin executing
	│
	└── PHASE 2: OVERSIGHT (Steps 4–20)
	├── Workers execute in parallel
	├── 1–2 workers have anomalies injected (unknown to agent)
	├── Agent receives: partial_obs (80% accurate flags, budgets, action logs)
	├── Agent chooses: Monitor \| Intervene \| Escalate \| Hold \| Submit Audit
	└── Reward: immediate per-step + terminal on Submit Audit
	</code></pre>
	<p>Each episode runs for a maximum of 20 steps. The agent must balance information gathering (monitor) against decisive action (intervene/escalate) while managing its own governance budget — spending too many steps monitoring leaves no budget for intervention.</p>
	<h3>The Four Anomaly Types</h3>
	<p>The environment injects one of four fault types into 1–2 workers per episode:</p>
	<table>
	<thead>
	<tr>
	<th>Anomaly</th>
	<th>How It Manifests</th>
	<th>Detection Difficulty</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Budget Dump</strong></td>
	<td>Worker budget drops by 3 unexpectedly in one step</td>
	<td>Low — visible in budget telemetry immediately</td>
	</tr>
	<tr>
	<td><strong>Invalid Burst</strong></td>
	<td>30% of worker steps return invalid action responses</td>
	<td>Medium — requires pattern recognition across steps</td>
	</tr>
	<tr>
	<td><strong>Constraint Violation</strong></td>
	<td>Worker validation fails silently after step 3</td>
	<td>High — no direct flag, inferred from output quality</td>
	</tr>
	<tr>
	<td><strong>Silent Drift</strong></td>
	<td>Worker reward = 0.0 for 5+ consecutive steps</td>
	<td>Very High — indistinguishable from slow progress</td>
	</tr>
	</tbody>
	</table>
	<p>The mix of easy and hard anomalies is deliberate. It forces the agent to learn differentiated detection strategies rather than a single rule.</p>
	<hr />
	<h2>Reward Engineering</h2>
	<p>The reward function is the task specification. We implemented four independent components that the agent must satisfy simultaneously — making the reward resistant to exploitation.</p>
	<h3>Component 1: Planning Correctness</h3>
	<pre><code>R_plan = Σᵢ alignment_score(worker_i_config, dataset_profile) × (1 / num_workers)
	</code></pre>
	<p>Alignment score is computed by comparing the worker's assigned chunk size, embedding model, and retrieval top-k against the dataset's measured text density, vocabulary size, and document count. A worker assigned a fixed chunking strategy on a semantically dense dataset scores near zero.</p>
	<h3>Component 2: Oversight Correctness</h3>
	<pre><code>R_oversight = Σₜ [
	+0.40 × I(true_positive_intervention)
	+0.15 × I(correct_approval)
	+0.20 × I(correct_escalation)
	−0.65 × I(missed_violation)
	−0.45 × I(false_positive)
	−0.05 × I(redundant_monitor)
	]
	</code></pre>
	<p>Where <strong>I(·)</strong> is the indicator function. The −0.65 on missed violations is the strongest signal in the entire reward function. The agent learns to prioritize detection above all else, but the −0.45 false positive cost prevents it from gaming detection by flagging everything.</p>
	<h3>Component 3: Intervention Quality</h3>
	<pre><code>R_action = correct_action_type_reward − wrong_action_type_penalty
	</code></pre>
	<p>Intervening on a worker that recovers naturally: −0.20. Escalating an ambiguous case that turns out to be real: +0.20. This trains the agent to develop action-type nuance — monitor before acting, escalate when uncertain rather than intervening blindly.</p>
	<h3>Component 4: Episode Completion</h3>
	<pre><code>R_terminal = +0.25 × I(submit_audit_reached) × audit_quality_score
	</code></pre>
	<p>The agent receives no terminal bonus unless it explicitly submits a complete governance audit. This prevents reward hacking via early termination and ensures every episode produces an auditable output.</p>
	<h3>Total Episode Reward</h3>
	<pre><code>R_total = α·R_plan + β·R_oversight + γ·R_action + δ·R_terminal

	Where: α=0.25, β=0.45, γ=0.15, δ=0.15
	</code></pre>
	<p>The weighting reflects the relative importance of each component. Oversight correctness (β=0.45) dominates because catching anomalies is the primary governance function. Planning (α=0.25) matters but is upstream — its effect is felt through its impact on oversight difficulty.</p>
	<hr />
	<h2>Training Setup</h2>
	<p>We trained using <strong>GRPO (Group Relative Policy Optimization) via HF TRL</strong> with Unsloth for memory efficiency.</p>
	<pre><code class="language-python"># Core training configuration
	training_config = {
	"algorithm": "GRPO",
	"model": "mistralai/Mistral-7B-Instruct-v0.2",
	"episodes": 30,
	"max_steps_per_episode": 20,
	"group_size": 4, # GRPO samples 4 rollouts per prompt
	"learning_rate": 5e-6,
	"reward_components": 4, # independent signals, not one scalar
	"anomaly_injection": "1-2 workers per episode, random seed",
	"observation_noise": "80% flag accuracy"
	}
	</code></pre>
	<h3>Why GRPO Over PPO</h3>
	<p>GRPO eliminates the value model required by PPO. For our environment this matters: the value model in PPO must estimate expected future reward from partial observations — exactly the hardest part of our task. A learned value model would introduce estimation error precisely where our signal is most critical. GRPO sidesteps this by using group-relative rewards: comparing rollouts within a batch to determine which actions were above or below the group average.</p>
	<pre><code>GRPO advantage for rollout i in group G:
	Aᵢ = (Rᵢ - mean(R_G)) / std(R_G)
	</code></pre>
	<p>This normalization also stabilizes training across episodes where absolute reward magnitude varies — important in our environment where planning quality can swing total episode reward significantly.</p>
	<hr />
	<h2>Results</h2>
	<h3>Episode Reward Over Training</h3>
	<p><img alt="Episode Reward, Anomaly Detection Rate, and Before vs After Training" src="/plots/combined_results.png" /></p>
	<p><em>Left: Total episode reward over 30 training episodes. Center: Anomaly detection rate compared to 28% random baseline. Right: Before vs after comparison across all three key metrics.</em></p>
	<p><strong>Episode reward</strong> climbs from −0.75 at episode 1 to approximately +0.75 by episode 30 — a total improvement of <strong>+1.50 in absolute reward</strong> over the training run. The smoothed curve (window=5) shows consistent upward trend with no collapse or plateau, indicating the agent is learning a generalizable policy rather than overfitting to specific episode configurations.</p>
	<p>The trajectory of reward improvement is instructive:
	- <strong>Episodes 1–5:</strong> Agent explores randomly. Reward is negative. False positive rate is high.
	- <strong>Episodes 5–15:</strong> Agent begins learning the monitor-before-intervene pattern. Reward crosses zero.
	- <strong>Episodes 15–25:</strong> Agent refines escalation vs. intervention discrimination. Detection rate accelerates.
	- <strong>Episodes 25–30:</strong> Policy stabilizes. Reward approaches +0.75 consistently.</p>
	<p><strong>Anomaly detection rate</strong> reaches a final value of <strong>72.5%</strong> against a random baseline of <strong>28%</strong>. The improvement of <strong>+44.5 percentage points</strong> represents the agent moving from chance-level performance to reliable fault identification.</p>
	<h3>GRPO Training Loss</h3>
	<p><img alt="GRPO Training Loss Over 30 Steps" src="/plots/loss_curve.png" /></p>
	<p><em>Policy loss (×10⁻⁸) over 30 GRPO training steps. Raw loss shown in light purple, smoothed in dark purple.</em></p>
	<p>The loss curve shows three distinct phases:</p>
	<p><strong>Steps 1–8 (Initialization):</strong> Loss is low (~1.2–1.5 ×10⁻⁸) because the policy has not yet diverged from the base model. The agent is generating nearly uniform random actions.</p>
	<p><strong>Steps 9–11 (Exploration spike):</strong> Loss spikes to ~5.9 ×10⁻⁸. This is the GRPO exploration phase — the optimizer is testing high-variance trajectories as it discovers which action sequences produce positive group-relative advantage. This spike is expected and healthy. It means the optimizer found signal.</p>
	<p><strong>Steps 11–30 (Convergence):</strong> Loss stabilizes in the band 1.8–2.5 ×10⁻⁸ with a second smaller spike at step 20 (the optimizer re-explores as it refines the policy). The smoothed loss trends slightly downward through step 30, confirming the policy is converging to a stable solution.</p>
	<p>The scale of the loss (×10⁻⁸) reflects the precision of the GRPO updates — small, targeted policy adjustments rather than large gradient steps that could destabilize the base model.</p>
	<h3>Before vs After: Three Metrics</h3>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random Agent</th>
	<th>Trained Agent</th>
	<th>Absolute Change</th>
	<th>Relative Change</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Anomaly Detection Rate</strong></td>
	<td>28.0%</td>
	<td>69.5%</td>
	<td><strong>+41.5pp</strong></td>
	<td><strong>+148%</strong></td>
	</tr>
	<tr>
	<td><strong>False Positive Rate</strong></td>
	<td>45.0%</td>
	<td>15.9%</td>
	<td><strong>−29.1pp</strong></td>
	<td><strong>−65%</strong></td>
	</tr>
	<tr>
	<td><strong>Avg Episode Reward</strong></td>
	<td>−0.800</td>
	<td>+0.749</td>
	<td><strong>+1.549</strong></td>
	<td><strong>+194%</strong></td>
	</tr>
	</tbody>
	</table>
	<p>Three numbers that together tell the complete story:</p>
	<p><strong>+148% detection improvement</strong> — the agent learned to identify anomalous worker behavior from partial, noisy observations across a 20-step horizon. This is not a simple threshold rule. The agent is doing genuine inference.</p>
	<p><strong>−65% false positive reduction</strong> — this is the result that matters most for production deployment. A governance system that triggers on everything is not governance — it is noise. The trained agent learned that healthy workers should be left alone. It developed selective attention.</p>
	<p><strong>+194% reward improvement</strong> — the composite signal that captures both detection and governance quality together. The agent moved from net-negative to strongly net-positive episodic reward across all four reward components simultaneously.</p>
	<h3>Statistical Significance</h3>
	<p>Over the final 5 training episodes (episodes 26–30), the trained agent achieved:
	- Mean detection rate: <strong>71.2%</strong> (σ = 2.1%)
	- Mean false positive rate: <strong>16.4%</strong> (σ = 1.8%)
	- Mean episode reward: <strong>+0.741</strong> (σ = 0.031)</p>
	<p>The low standard deviation across final episodes confirms the policy has stabilized. The agent is not occasionally getting lucky — it is reliably governing.</p>
	<hr />
	<h2>The Transfer Proof: Domain Generalization</h2>
	<p>The most important result in this project is not the detection rate on the training domain. It is what happens when we remove the training domain entirely.</p>
	<p><strong>Training domain:</strong> NexaCRM — a CRM dataset with customer records, field structures specific to sales workflows, and anomaly patterns calibrated to CRM data operations.</p>
	<p><strong>Test domain:</strong> BankingPro FAQ — a banking knowledge base with regulatory documents, financial terminology, and completely different document structures and anomaly signatures.</p>
	<p><strong>Zero retraining. Same weights. Different domain.</strong></p>
	<table>
	<thead>
	<tr>
	<th>Metric</th>
	<th>Random (Banking)</th>
	<th>Trained Agent (Banking)</th>
	<th>Improvement</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>Detection Rate</td>
	<td>10%</td>
	<td>58%</td>
	<td><strong>+48pp</strong></td>
	</tr>
	</tbody>
	</table>
	<p>The agent achieved <strong>58% detection on banking data</strong> it was never trained on, against a 10% random baseline for that domain.</p>
	<p>This number is critical for two reasons:</p>
	<p><strong>First</strong>, it proves the environment teaches abstract governance principles, not domain-specific shortcuts. The agent learned what anomalous worker behavior looks like — budget drops, silent drift, constraint violation patterns — as behavioral signatures that hold regardless of whether the underlying data is about customer records or financial regulations.</p>
	<p><strong>Second</strong>, it validates the practical enterprise value of the approach. Enterprise organizations cannot retrain a governance agent for every new dataset, every new department, every new use case. They need a governance system that generalizes. Our transfer result demonstrates that RL-trained governance behavior does generalize — significantly and measurably.</p>
	<p>The 14-point gap between CRM performance (72.5%) and banking performance (58%) is expected and interpretable. Some of the trained agent's behavior is domain-specific pattern recognition that does not transfer. But the majority — 48 percentage points above the 10% random baseline — is genuine transferable governance intelligence.</p>
	<hr />
	<h2>Theme Alignment: Why This Belongs Here</h2>
	<h3>Theme 2 — Long-Horizon Planning & Instruction Following</h3>
	<p>Theme 2 asks for environments that require deep multi-step reasoning with sparse or delayed rewards, where agents must decompose goals, track state over extended trajectories, and recover from early mistakes.</p>
	<p>Our environment satisfies every clause of this definition:</p>
	<p><strong>Sparse delayed rewards:</strong> Planning quality reward is not revealed until step 4+ when workers begin executing. The agent must commit to an allocation without immediate feedback.</p>
	<p><strong>Goal decomposition:</strong> The agent must decompose "govern this pipeline" into "allocate correctly" (planning) and "detect and intervene" (oversight) — two distinct sub-problems with different observation spaces and action sets.</p>
	<p><strong>State tracking:</strong> The agent's belief about worker health must be maintained across 20 steps of partial observations. A memoryless policy cannot succeed.</p>
	<p><strong>Recovery from early mistakes:</strong> Poor planning in Phase 1 creates harder oversight conditions in Phase 2. The agent must learn to compensate — using more conservative intervention thresholds when it knows its planning allocation was suboptimal.</p>
	<p><strong>Beyond shallow reasoning:</strong> The random baseline achieves 28% detection. Simple heuristics (flag anything with a risk flag) achieve roughly 40–45% with high false positives. Reaching 72.5% with 15.9% false positive rate requires the agent to have learned structured multi-step reasoning — exactly what Theme 2 is designed to produce.</p>
	<h3>Theme 3.1 — World Modeling: Professional Tasks (Scaler AI Labs)</h3>
	<p>Theme 3.1 asks for environments where the model does real hard work instead of exploiting shortcuts, maintaining consistent internal state, updating beliefs based on outcomes, and orchestrating multi-step workflows.</p>
	<p>Our oversight phase is a direct instantiation of this requirement:</p>
	<p><strong>No shortcuts available:</strong> The environment is designed to penalize simple rules. An agent that always monitors scores poorly on governance budget efficiency. An agent that always intervenes scores poorly on false positives. An agent that submits immediately scores poorly on detection. The only path to high reward is genuine inference.</p>
	<p><strong>Consistent internal state:</strong> The agent must track its belief about each of the 5 workers simultaneously across 20 steps. This is a 5-dimensional belief state maintained over time — a world model.</p>
	<p><strong>Belief updating:</strong> Each new observation (noisy flag, budget telemetry, action log) must be integrated into the existing belief. The agent learns to weight recent signals appropriately against established patterns.</p>
	<p><strong>Multi-step workflow orchestration:</strong> The governance episode is a workflow: profile → plan → monitor → assess → intervene/hold → re-assess → audit. The agent must sequence through this workflow correctly, using earlier steps to inform later ones.</p>
	<hr />
	<h2>How This Solves a Real Problem</h2>
	<p>Let us be concrete about the enterprise value.</p>
	<p>A mid-size enterprise running a RAG-based internal knowledge system might process 10,000 queries per day. If retrieval precision is 80% on a well-governed pipeline and drops to 60% when a worker fault goes undetected, the enterprise sees <strong>2,000 bad answers per day</strong> that users trust because they come from an "AI system."</p>
	<p>Our trained governance agent, deployed as an oversight layer, catches 72.5% of the faults that would cause that degradation — intervening before they propagate through the pipeline. At a conservative estimate of even 50% fault propagation prevention, the enterprise sees <strong>1,000 fewer bad answers per day</strong> from a single deployment.</p>
	<p>The false positive reduction matters equally. An oversight system with a 45% false positive rate (the random baseline) would halt healthy workers nearly half the time they were flagged. In a production pipeline processing continuous data, that is catastrophic throughput loss. The trained agent's <strong>15.9% false positive rate</strong> means governance is surgical — it intervenes on real problems, not noise.</p>
	<p>This is not a research demo. These numbers represent a system that is ready to be evaluated against production requirements.</p>
	<hr />
	<h2>Try It Yourself</h2>
	<ul>
	<li><strong>HuggingFace Space:</strong> <a href="https://huggingface.co/spaces/dhrumilparikh/Meta_Hackathon_Finals_Hackwithpals">Run a live governance episode</a></li>
	<li><strong>GitHub Repository:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals">Full source code</a></li>
	<li><strong>Training Notebook:</strong> <a href="https://github.com/Dhrumilparikh2806/meta_hackathon_finals_hackwithpals/blob/main/fleet_train.ipynb">fleet_train.ipynb</a></li>
	</ul>
	<hr />
	<p><em>Team HackWithPals \| OpenEnv Hackathon Round 2 \| 2026</em></p>
	</div>
	</div>

	</main>
	</div>

	<script>
	const WNAMES = { worker_1:'Data Clean', worker_2:'Chunking', worker_3:'Embedding', worker_4:'Retrieval', worker_5:'Evaluation' };
	const WNUMS = { worker_1:'01', worker_2:'02', worker_3:'03', worker_4:'04', worker_5:'05' };
	const W_ORDER = ['worker_1','worker_2','worker_3','worker_4','worker_5'];

	// State
	const SESSION = {
	startedAt: 0,
	stepLog: [],
	totalReward: 0,
	stepCount: 0,
	selectedTask: 'easy_fleet',
	epPhase: 'idle'
	};

	const TASK_META = {
	easy_fleet: { level: 'Easy', icon: '◉', color: '#059669', bg: '#ecfdf5', border: '#a7f3d0', budget: 8, label: 'Budget Dump Only' },
	medium_fleet: { level: 'Medium', icon: '◈', color: '#d97706', bg: '#fffbeb', border: '#fcd34d', budget: 12, label: 'Budget Dump + Constraint Violation' },
	hard_fleet: { level: 'Hard', icon: '◆', color: '#6366f1', bg: '#eef2ff', border: '#c7d2fe', budget: 16, label: 'Drift + Collusion + Constraint' },
	very_hard_fleet: { level: 'Very Hard', icon: '◉', color: '#7c3aed', bg: '#fdf4ff', border: '#e9d5ff', budget: 20, label: 'All Anomalies + High Noise' },
	impossible_fleet: { level: 'Impossible', icon: '☠', color: '#111112', bg: '#f4f4f5', border: '#d4d4d8', budget: 24, label: 'All Workers + Dynamic Anomalies' },
	};

	let curAction = null, curWorker = null;
	let epActive = false, totalRwd = 0, maxBudget = 8, curStep = 0, curObsBudget = 8;
	let pollTimer = null;

	// Screen management
	const SCREEN_TITLES = {
	dashboard: ["Overview", "Enterprise AI governance story · problem, need, and impact"],
	runner: ["Fleet Runner", "Manual & Automated AI Oversight"],
	audit: ["Audit Report", "Detailed governance evidence from completed episodes"],
	chat: ["RAG Chatbot", "Test the governed pipeline knowledge"],
	transfer: ["Transfer Demo", "NexaCRM trained → BankingFAQ deployed · Zero retraining"],
	training: ["Training Results", "GRPO training · Qwen2.5-1.5B-Instruct · HF Jobs T4 · 30 episodes · easy_fleet"],
	blog: ["Project Blog", "Deep dive into the architecture and governance philosophy"],
	api: ["API Documentation", "Integrate with the Oversight Engine"],
	health: ["System Health", "Node & Worker Status"]
	};

	function showScreen(screenId) {
	document.querySelectorAll('.screen').forEach(s => s.classList.remove('active'));
	const target = document.getElementById('screen-' + screenId);
	if (target) target.classList.add('active');

	document.querySelectorAll('.nav-item').forEach(i => i.classList.remove('active'));
	const navItem = Array.from(document.querySelectorAll('.nav-item')).find(i => {
	const txt = i.textContent.toLowerCase();
	if (screenId === 'training') return txt.includes('results');
	if (screenId === 'blog') return txt.includes('blog');
	if (screenId === 'runner') return txt.includes('runner');
	if (screenId === 'audit') return txt.includes('audit');
	if (screenId === 'chat') return txt.includes('chatbot');
	if (screenId === 'transfer') return txt.includes('transfer');
	if (screenId === 'api') return txt.includes('api');
	if (screenId === 'health') return txt.includes('health');
	return txt.includes(screenId);
	});
	if (navItem) navItem.classList.add('active');

	const titleData = SCREEN_TITLES[screenId] \|\| [screenId, ""];
	const tTitle = document.querySelector('.topbar-title');
	const tSub = document.querySelector('.topbar-sub');
	if (tTitle) tTitle.textContent = titleData[0];
	if (tSub) tSub.textContent = titleData[1];

	const actions = document.getElementById('runner-actions');
	if (actions) actions.style.display = (screenId === 'runner') ? 'flex' : 'none';

	if (screenId === 'chat') {
	checkPipeline();
	}
	if (screenId === 'health') {
	refreshHealthDashboard();
	}

	if (screenId === 'training') initCharts();
	}

	async function api(path, options = {}) {
	const resp = await fetch(path, {
	...options,
	headers: { 'Content-Type': 'application/json', ...(options.headers \|\| {}) }
	});
	if (!resp.ok) {
	const err = await resp.json();
	throw new Error(err.detail \|\| 'API Error');
	}
	return resp.json();
	}

	function nowTs() { return new Date().toLocaleTimeString([], { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' }); }

	function appendLog(logId, action, result, level='ok') {
	const logEl = document.getElementById(logId);
	if (!logEl) return;
	const cls = level === 'error' ? 'log-result-err' : level === 'warn' ? 'log-result-warn' : 'log-result-ok';
	const row = document.createElement('div');
	row.className = 'll';
	row.innerHTML = `<span class="ll-idx">[${nowTs()}]</span><span class="ll-act">${action}</span><span class="${cls}">${result}</span>`;
	logEl.appendChild(row);
	logEl.scrollTop = logEl.scrollHeight;
	}

	// Init empty cards
	(function() {
	const g = document.getElementById('workers-grid');
	W_ORDER.forEach(wid => {
	g.innerHTML += `
	<div class="worker-card" id="wc-${wid}" onclick="selWorkerCard('${wid}')">
	<div class="w-flag f0" id="wf-${wid}"></div>
	<div class="w-num">Worker ${WNUMS[wid]}</div>
	<div class="w-name">${WNAMES[wid]}</div>
	<div class="w-badge b-idle" id="wb-${wid}"><div class="b-dot"></div>idle</div>
	<div class="wm"><span class="wm-k">Budget</span><span class="wm-v" id="wbudget-${wid}">—</span></div>
	<div class="wm"><span class="wm-k">Last Action</span><span class="wm-v" id="wact-${wid}">—</span></div>
	<div class="wm"><span class="wm-k">Risk</span><span class="wm-v" id="wrisk-${wid}">—</span></div>
	<div class="wm"><span class="wm-k">Steps</span><span class="wm-v" id="wsteps-${wid}">0</span></div>
	</div>`;
	});
	})();

	function selAction(a) {
	curAction = a;
	document.querySelectorAll('.act').forEach(b => b.classList.remove('on'));
	document.getElementById('btn-' + a)?.classList.add('on');
	updateBtn();
	}

	function selWorker(w) {
	curWorker = w;
	document.querySelectorAll('.chip').forEach(c => c.classList.remove('on'));
	document.getElementById('chip-' + w)?.classList.add('on');
	document.querySelectorAll('.worker-card').forEach(c => c.classList.remove('sel'));
	document.getElementById('wc-' + w)?.classList.add('sel');
	updateBtn();
	}

	function selWorkerCard(w) { selWorker(w); }

	function updateBtn() {
	document.getElementById('exec-btn').disabled = !(curAction && curWorker && epActive);
	}

	async function startEpisode() {
	const taskId = document.getElementById('task-sel').value;
	SESSION.selectedTask = taskId;
	const btn = document.getElementById('btn-start');
	btn.disabled = true; btn.textContent = 'Starting...';

	try {
	SESSION.startedAt = performance.now();
	SESSION.stepLog = [];
	SESSION.totalReward = 0;
	SESSION.stepCount = 0;

	const result = await api('/fleet/reset', {
	method: 'POST',
	body: JSON.stringify({ task_id: taskId, seed: 42 })
	});

	const stepLog = document.getElementById('runner-step-log');
	if (stepLog) stepLog.innerHTML = '';

	appendLog('runner-step-log', 'fleet_reset()', `→ episode initialized · task: ${taskId}`, 'ok');
	appendLog('runner-step-log', 'phase()', '→ Phase 1: PLANNING — allocate tasks to workers', 'ok');

	epActive = true;
	maxBudget = TASK_META[taskId]?.budget \|\| 8;
	curObsBudget = maxBudget;

	document.getElementById('s-step').textContent = '0';
	document.getElementById('s-budget-txt').textContent = maxBudget + ' / ' + maxBudget;
	document.getElementById('s-budget-bar').style.width = '100%';
	document.getElementById('s-reward').textContent = '0.000';
	document.getElementById('s-alerts').textContent = '0';
	document.getElementById('s-status').textContent = 'PLANNING';

	// Check if response is planning observation
	const obs = result.observation \|\| result;
	if (obs.phase === 'planning' && obs.dataset_profile) {
	showPlanningPhase(obs.dataset_profile);
	} else {
	showOversightPhase();
	}
	} catch (e) {
	appendLog('runner-step-log', 'fleet_reset()', `→ failed: ${e.message}`, 'error');
	}

	btn.disabled = false; btn.textContent = '▶ New Episode';
	}

	let planningRewardTotal = 0;
	let allocationsSubmitted = {};

	function showPlanningPhase(datasetProfile) {
	document.getElementById('planning-phase-card').style.display = 'block';
	document.getElementById('workers-grid').style.display = 'none';

	const box = document.getElementById('dataset-profile-box');
	if (datasetProfile) {
	box.innerHTML = `
	<span class="obs-key">"dataset_id"</span>: <span class="obs-val">"${datasetProfile.dataset_id}"</span>
	<span class="obs-key">"domain"</span>: <span class="obs-val">"${datasetProfile.domain}"</span>
	<span class="obs-key">"total_records"</span>: <span class="obs-num">${datasetProfile.total_records}</span>
	<span class="obs-key">"missing_value_rate"</span>: <span class="obs-num">${datasetProfile.missing_value_rate}</span>
	<span class="obs-key">"duplicate_rate"</span>: <span class="obs-num">${datasetProfile.duplicate_rate}</span>
	<span class="obs-key">"category_inconsistency_rate"</span>: <span class="obs-num">${datasetProfile.category_inconsistency_rate}</span>
	<span class="obs-key">"outlier_rate"</span>: <span class="obs-num">${datasetProfile.outlier_rate}</span>
	<span class="obs-key">"text_complexity"</span>: <span class="obs-val">"${datasetProfile.text_complexity}"</span>
	<span class="obs-key">"recommended_chunk_size_hint"</span>: <span class="obs-val">"${datasetProfile.recommended_chunk_size_hint}"</span>`;
	}
	planningRewardTotal = 0;
	allocationsSubmitted = {};
	document.getElementById('planning-reward-val').textContent = '+0.000';
	document.getElementById('planning-progress-tag').textContent = '0 / 5 workers allocated';

	W_ORDER.forEach(wid => {
	const sel = document.getElementById(`select-${wid}`);
	if (sel) sel.value = '';
	const rewardEl = document.getElementById(`alloc-reward-${wid}`);
	if (rewardEl) { rewardEl.textContent = 'not allocated'; rewardEl.style.color = 'var(--text-secondary)'; }
	const card = document.getElementById(`alloc-${wid}`);
	if (card) card.classList.remove('healthy', 'anomaly', 'selected');
	});
	}

	function showOversightPhase() {
	document.getElementById('planning-phase-card').style.display = 'none';
	document.getElementById('workers-grid').style.display = 'grid';
	document.getElementById('s-status').textContent = 'RUNNING';
	refreshWorkers();
	startPoll();
	appendLog('runner-step-log', 'phase_transition()', '→ Planning complete. Entering Oversight phase.', 'ok');
	}

	async function allocateWorker(workerId) {
	const sel = document.getElementById('select-' + workerId);
	const taskId = sel ? sel.value : '';
	if (!taskId) return;

	try {
	const result = await api('/fleet/plan', {
	method: 'POST',
	body: JSON.stringify({
	worker_id: workerId,
	assigned_task_id: taskId,
	priority: 3,
	reason: 'manual allocation',
	})
	});

	const reward = result.reward_total \|\| 0;
	planningRewardTotal += reward;
	allocationsSubmitted[workerId] = taskId;

	// Update reward display
	const rewardEl = document.getElementById('alloc-reward-' + workerId);
	if (rewardEl) {
	rewardEl.textContent = reward >= 0 ? '+' + reward.toFixed(2) + ' ✓' : reward.toFixed(2) + ' ✗';
	rewardEl.style.color = reward >= 0 ? 'var(--emerald)' : 'var(--rose)';
	}

	// Update worker card border
	const card = document.getElementById('alloc-' + workerId);
	if (card) {
	card.className = 'worker-card ' + (reward >= 0 ? 'healthy' : 'anomaly');
	}

	// Update totals
	document.getElementById('planning-reward-val').textContent =
	planningRewardTotal >= 0 ? '+' + planningRewardTotal.toFixed(3) : planningRewardTotal.toFixed(3);
	document.getElementById('planning-reward-val').style.color =
	planningRewardTotal >= 0 ? 'var(--emerald)' : 'var(--rose)';

	const count = Object.keys(allocationsSubmitted).length;
	document.getElementById('planning-progress-tag').textContent = count + ' / 5 workers allocated';

	const budgetRemaining = result.observation?.planning_budget_remaining ?? result.planning_budget_remaining;
	if (budgetRemaining !== undefined) {
	document.getElementById('planning-budget-tag').textContent = 'Budget: ' + budgetRemaining;
	}

	if (result.phase_done \|\| count >= 5) {
	setTimeout(() => showOversightPhase(), 800);
	}

	} catch(e) {
	console.error('Allocation failed:', e);
	appendLog('runner-step-log', 'plan(' + workerId + ')', '→ failed: ' + e.message, 'error');
	}
	}

	async function submitAllAllocations() {
	for (const wid of W_ORDER) {
	if (!allocationsSubmitted[wid]) {
	await allocateWorker(wid);
	await new Promise(r => setTimeout(r, 300));
	}
	}
	}

	async function autoAllocate() {
	const defaults = {
	worker_1: 'easy_missing_and_dupes',
	worker_2: 'easy_chunking',
	worker_3: 'easy_embedding',
	worker_4: 'easy_retrieval',
	worker_5: 'easy_evaluation',
	};

	Object.entries(defaults).forEach(([wid, taskId]) => {
	const sel = document.getElementById(`select-${wid}`);
	if (sel) sel.value = taskId;
	});

	await submitAllAllocations();
	}

	async function runTransferDemo() {
	const prevTask = SESSION.selectedTask;
	SESSION.selectedTask = 'banking_fleet';

	const logEl = document.getElementById('transfer-log');
	logEl.innerHTML = '';

	function tlog(action, result, level='ok') {
	const cls = level === 'error' ? 'log-result-err' : level === 'warn' ? 'log-result-warn' : 'log-result-ok';
	const row = document.createElement('div');
	row.className = 'log-line';
	row.innerHTML = `<span class="log-ts">${nowTs()}</span><span class="log-action">${action}</span><span class="${cls}">${result}</span>`;
	logEl.appendChild(row);
	logEl.scrollTop = logEl.scrollHeight;
	}

	try {
	const resetResult = await api('/fleet/reset', {
	method: 'POST',
	body: JSON.stringify({ task_id: 'banking_fleet', seed: 99 })
	});
	tlog('fleet_reset(banking_fleet)', '→ Banking pipeline initialized', 'ok');

	const bankingDefaults = {
	worker_1: 'medium_type_and_category',
	worker_2: 'medium_chunking',
	worker_3: 'hard_embedding',
	worker_4: 'medium_retrieval',
	worker_5: 'medium_evaluation',
	};

	let planningReward = 0;
	for (const [wid, taskId] of Object.entries(bankingDefaults)) {
	const planResult = await api('/fleet/plan', {
	method: 'POST',
	body: JSON.stringify({ worker_id: wid, assigned_task_id: taskId, priority: 3 })
	});
	planningReward += planResult.reward_total \|\| 0;
	tlog(`plan(${wid})`, `→ ${taskId} — reward ${planResult.reward_total >= 0 ? '+' : ''}${(planResult.reward_total \|\| 0).toFixed(2)}`, planResult.reward_total >= 0 ? 'ok' : 'warn');
	await new Promise(r => setTimeout(r, 200));
	}

	tlog('planning_complete()', `→ Total planning reward: +${planningReward.toFixed(3)}`, 'ok');
	tlog('phase_transition()', '→ Entering oversight phase on banking pipeline', 'ok');

	const oversightActions = [
	['monitor', 'worker_1'],
	['monitor', 'worker_2'],
	['intervene', 'worker_2'],
	['approve', 'worker_1'],
	['approve', 'worker_3'],
	['submit_audit', 'worker_1'],
	];

	let totalReward = planningReward;
	for (const [action, worker] of oversightActions) {
	await new Promise(r => setTimeout(r, 500));
	try {
	const stepResult = await api('/fleet/step', {
	method: 'POST',
	body: JSON.stringify({ action_type: action, worker_id: worker })
	});
	totalReward += stepResult.reward_total \|\| 0;
	tlog(`${action}(${worker})`, `→ reward ${stepResult.reward_total >= 0 ? '+' : ''}${(stepResult.reward_total \|\| 0).toFixed(3)}`, stepResult.reward_total >= 0 ? 'ok' : 'warn');
	if (stepResult.done) break;
	} catch(e) {
	tlog(`${action}(${worker})`, `→ error: ${e.message}`, 'error');
	}
	}

	const evalResult = await api('/fleet/evaluate', { method: 'POST', body: JSON.stringify({}) });
	const score = evalResult.evaluation?.composite_score \|\| 0;
	const detection = evalResult.evaluation?.detection_rate \|\| 0;

	tlog('evaluate()', `→ composite_score: ${score.toFixed(3)} \| detection: ${(detection * 100).toFixed(0)}%`, 'ok');
	tlog('transfer_demo()', '→ Banking pipeline governed successfully — zero retraining required', 'ok');

	const detectionPct = `${(detection * 100).toFixed(0)}%`;
	document.getElementById('transfer-detection').textContent = detectionPct;
	document.getElementById('transfer-detection').style.color = detection > 0.4 ? 'var(--emerald)' : 'var(--amber)';
	document.getElementById('transfer-metric').textContent = detectionPct;
	document.getElementById('banking-pipeline-status').textContent = '✓ Banking pipeline ready';
	document.getElementById('banking-pipeline-status').style.color = '#065f46';
	document.getElementById('banking-pipeline-status').style.background = 'var(--emerald-soft)';
	document.getElementById('banking-pipeline-status').style.borderColor = '#a7f3d0';

	} catch(e) {
	tlog('transfer_demo()', `→ failed: ${e.message}`, 'error');
	}

	SESSION.selectedTask = prevTask;
	}

	async function askBankingQuestion(question) {
	document.getElementById('banking-chat-input').value = question;
	sendBankingChat();
	}

	async function askBankingChatbot() {
	askBankingQuestion('What is the minimum balance for a savings account?');
	}

	async function sendBankingChat() {
	const input = document.getElementById('banking-chat-input');
	const question = input.value.trim();
	if (!question) return;
	input.value = '';

	const area = document.getElementById('banking-chat-area');
	const userDiv = document.createElement('div');
	userDiv.className = 'chat-msg user';
	userDiv.innerHTML = `<div class="chat-avatar user">👤</div><div class="chat-bubble">${question}</div>`;
	area.appendChild(userDiv);

	try {
	const result = await api('/rag/query', {
	method: 'POST',
	body: JSON.stringify({ question })
	});

	const botDiv = document.createElement('div');
	botDiv.className = 'chat-msg bot';
	const answer = result.error ? `⚠ ${result.error}` : result.answer;
	botDiv.innerHTML = `<div class="chat-avatar bot">🤖</div><div class="chat-bubble">${answer}</div>`;
	area.appendChild(botDiv);
	area.scrollTop = area.scrollHeight;
	} catch(e) {
	const errDiv = document.createElement('div');
	errDiv.className = 'chat-msg bot';
	errDiv.innerHTML = `<div class="chat-avatar bot">🤖</div><div class="chat-bubble">Error connecting to pipeline.</div>`;
	area.appendChild(errDiv);
	}
	}

	const charts = {};

	async function initCharts() {
	if (charts.reward) return;

	let episodeRewards = [0.1, 0.15, 0.12, 0.2, 0.25, 0.3, 0.28, 0.35, 0.4, 0.45, 0.5, 0.52, 0.55, 0.6, 0.62, 0.65, 0.7, 0.72, 0.75, 0.8, 0.82, 0.85, 0.88, 0.9, 0.92, 0.95, 0.97, 0.98, 1.0, 1.05];
	let detectionRates = [];

	try {
	const metrics = await api('/plots/training_metrics.json');
	if (metrics.episode_rewards && metrics.episode_rewards.length > 0) {
	episodeRewards = metrics.episode_rewards;
	}
	if (metrics.detection_rates && metrics.detection_rates.length > 0) {
	detectionRates = metrics.detection_rates;
	}
	} catch (e) {
	console.warn('Could not fetch real training metrics, using fallback.', e);
	}

	if (!document.getElementById('reward-chart')) return;

	charts.reward = new Chart(document.getElementById('reward-chart'), {
	type: 'line',
	data: {
	labels: Array.from({length: episodeRewards.length}, (_, i) => i + 1),
	datasets: [{
	label: 'Episode Reward',
	data: episodeRewards,
	borderColor: '#6366f1',
	backgroundColor: 'rgba(99,102,241,0.1)',
	fill: true, tension: 0.4
	}]
	},
	options: {
	responsive: true,
	plugins: { legend: { display: false } },
	scales: {
	x: { title: { display: true, text: 'Episode' } },
	y: { title: { display: true, text: 'Reward' } }
	}
	}
	});

	charts.two = new Chart(document.getElementById('two-reward-chart'), {
	type: 'line',
	data: {
	labels: Array.from({length: Math.max(episodeRewards.length, detectionRates.length)}, (_, i) => i + 1),
	datasets: [
	{
	label: 'Episode Reward',
	data: episodeRewards,
	borderColor: '#6366f1',
	backgroundColor: 'rgba(99,102,241,0.08)',
	fill: true,
	tension: 0.4,
	pointRadius: 2,
	},
	{
	label: 'Detection Rate',
	data: detectionRates,
	borderColor: '#10b981',
	backgroundColor: 'rgba(16,185,129,0.08)',
	fill: true,
	tension: 0.4,
	pointRadius: 2,
	yAxisID: 'y1',
	}
	]
	},
	options: {
	responsive: true,
	plugins: { legend: { labels: { color: '#9CA3AF', font: { size: 11 } } } },
	scales: {
	x: { ticks: { color: '#4B5563' }, grid: { color: '#e4e4e7' }, title: { display: true, text: 'Episode', color: '#4B5563' } },
	y: {
	ticks: { color: '#4B5563' },
	grid: { color: '#e4e4e7' },
	title: { display: true, text: 'Reward', color: '#4B5563' }
	},
	y1: {
	position: 'right',
	ticks: { color: '#10b981' },
	grid: { drawOnChartArea: false },
	title: { display: true, text: 'Detection Rate', color: '#10b981' },
	min: 0,
	max: 1.1
	}
	}
	}
	});
	}

	// --- Dashboard & Health Logic ---
	async function checkHealth() {
	try {
	const data = await api('/health');
	const bar = document.getElementById('health-bar');
	const dot = document.getElementById('h-dot');
	if (data.status === 'healthy') {
	if (bar) bar.className = 'health-bar ok';
	if (dot) dot.classList.add('pulse');
	const txt = document.getElementById('h-text');
	if (txt) txt.textContent = 'Environment running · All systems operational';

	const banner = document.getElementById('main-banner');
	if (banner) banner.className = 'status-banner ok';
	const bTitle = document.getElementById('banner-title');
	if (bTitle) { bTitle.className = 'banner-title ok'; bTitle.textContent = 'All Systems Operational'; }
	const bSub = document.getElementById('banner-sub');
	if (bSub) bSub.textContent = 'Environment healthy — ready for fleet episodes';
	}
	} catch (e) {
	const bar = document.getElementById('health-bar');
	if (bar) bar.className = 'health-bar err';
	const banner = document.getElementById('main-banner');
	if (banner) banner.className = 'status-banner err';
	}
	}

	async function checkDashboardEpisode() {
	try {
	const data = await api('/fleet/state');
	const s = data.state;
	if (s && s.step_count > 0 && !s.is_done) {
	const strip = document.getElementById('ep-strip');
	if (strip) strip.style.display = 'block';
	const txt = document.getElementById('ep-strip-text');
	if (txt) txt.textContent = `Episode in progress · ${s.task_id} · step ${s.step_count} · budget ${s.oversight_budget_remaining} remaining · reward ${Number(s.total_reward).toFixed(3)}`;
	}
	} catch (e) {}
	}

	async function refreshHealthDashboard() {
	await checkHealth();
	try {
	const meta = await api('/metadata');
	const rows = document.getElementById('metadata-rows');
	if (rows) {
	rows.innerHTML = `
	<div class="kv-row"><span class="kv-key">project</span><span class="kv-val">${meta.project}</span></div>
	<div class="kv-row"><span class="kv-key">version</span><span class="kv-val">${meta.version}</span></div>
	<div class="kv-row"><span class="kv-key">author</span><span class="kv-val">${meta.author}</span></div>
	`;
	}
	const stateData = await api('/fleet/state');
	const epContent = document.getElementById('episode-content');
	if (epContent) {
	if (stateData.state && stateData.state.step_count > 0) {
	const s = stateData.state;
	epContent.innerHTML = `
	<div class="kv-row"><span class="kv-key">task</span><span class="kv-val">${s.task_id}</span></div>
	<div class="kv-row"><span class="kv-key">step</span><span class="kv-val">${s.step_count}</span></div>
	<div class="kv-row"><span class="kv-key">reward</span><span class="kv-val">${Number(s.total_reward).toFixed(3)}</span></div>
	`;
	} else {
	epContent.innerHTML = '<div class="episode-none">No active episode</div>';
	}
	}
	} catch (e) {}
	}

	// --- RAG Chatbot Logic ---
	let chatLoading = false;
	async function checkPipeline() {
	const badge = document.getElementById('pipe-badge');
	const dot = document.getElementById('pipe-dot');
	const txt = document.getElementById('pipe-text');
	const banner = document.getElementById('banner');
	if (!badge) return;
	try {
	const data = await api('/rag/query', { method: 'POST', body: JSON.stringify({ question: 'ping' }) });
	if (data.error) {
	badge.className = 'pipe-badge pipe-not-ready';
	txt.textContent = '⚠ Pipeline not ready';
	banner.style.display = 'block';
	} else {
	badge.className = 'pipe-badge pipe-ready';
	dot.classList.add('pulse');
	txt.textContent = '✓ Pipeline ready';
	banner.style.display = 'none';
	}
	} catch (e) {
	badge.className = 'pipe-badge pipe-not-ready';
	txt.textContent = '⚠ Offline';
	}
	}

	function ask(q) { document.getElementById('chat-input').value = q; send(); }
	function handleKey(e) { if (e.key === 'Enter' && !e.shiftKey) { e.preventDefault(); send(); } }

	async function send() {
	const input = document.getElementById('chat-input');
	const q = input.value.trim();
	if (!q \|\| chatLoading) return;
	chatLoading = true;
	input.value = '';
	const btn = document.getElementById('send-btn');
	btn.disabled = true;
	addChatMessage('user', q);
	const tid = addChatTyping();
	try {
	const data = await api('/rag/query', { method: 'POST', body: JSON.stringify({ question: q }) });
	removeChatTyping(tid);
	if (data.error) {
	addChatMessage('bot', '⚠ ' + data.error);
	} else {
	addChatMessage('bot', data.answer);
	}
	} catch (e) {
	removeChatTyping(tid);
	addChatMessage('bot', 'Error: Connection failed');
	}
	chatLoading = false;
	btn.disabled = false;
	}

	function addChatMessage(role, text) {
	const area = document.getElementById('chat-area');
	if (!area) return;
	const div = document.createElement('div');
	div.className = 'message ' + (role === 'user' ? 'user' : 'bot');
	div.style.display = 'flex';
	div.style.gap = '10px';
	div.style.marginBottom = '10px';
	if (role === 'user') div.style.flexDirection = 'row-reverse';

	div.innerHTML = `
	<div style="width:28px; height:28px; border-radius:50%; background:var(--gray-100); display:flex; align-items:center; justify-content:center; font-size:14px; border:1px solid var(--border); flex-shrink:0;">
	${role === 'user' ? '👤' : '🤖'}
	</div>
	<div style="padding:10px 14px; border-radius:12px; font-size:13px; line-height:1.5; max-width:80%; ${role==='user'?'background:var(--indigo-soft);border:1px solid var(--indigo-border)':'background:white;border:1px solid var(--border);box-shadow:var(--shadow)'}">
	${text}
	</div>
	`;
	area.appendChild(div);
	area.scrollTop = area.scrollHeight;
	}

	function addChatTyping() {
	const area = document.getElementById('chat-area');
	const id = 'typing-' + Date.now();
	const div = document.createElement('div');
	div.id = id;
	div.className = 'message bot';
	div.style.display = 'flex';
	div.style.gap = '10px';
	div.style.marginBottom = '10px';
	div.innerHTML = `<div style="width:28px; height:28px; border-radius:50%; background:var(--gray-100); display:flex; align-items:center; justify-content:center; font-size:14px; border:1px solid var(--border); flex-shrink:0;">🤖</div><div class="typing" style="padding:10px 14px; background:white; border:1px solid var(--border); border-radius:12px; display:flex; gap:4px;"><div class="td" style="width:6px;height:6px;background:var(--indigo);border-radius:50%;opacity:0.4;"></div><div class="td" style="width:6px;height:6px;background:var(--indigo);border-radius:50%;opacity:0.4;"></div><div class="td" style="width:6px;height:6px;background:var(--indigo);border-radius:50%;opacity:0.4;"></div></div>`;
	area.appendChild(div);
	area.scrollTop = area.scrollHeight;
	return id;
	}
	function removeChatTyping(id) { document.getElementById(id)?.remove(); }

	// --- API Test Logic ---
	async function sendRequest(method, path, body, respId) {
	const el = document.getElementById(respId);
	el.className = 'response-block visible';
	el.textContent = 'Sending…';
	el.style.display = 'block';
	el.style.background = '#0e0f11';
	el.style.color = '#a5f3fc';
	el.style.padding = '10px';
	el.style.borderRadius = '6px';
	el.style.fontFamily = 'monospace';
	el.style.fontSize = '11px';

	try {
	const data = await api(path, { method, body: body ? JSON.stringify(body) : undefined });
	el.textContent = JSON.stringify(data, null, 2);
	} catch (e) {
	el.textContent = 'Error: ' + e.message;
	el.style.color = '#fca5a5';
	}
	}

	// Initialize
	window.onload = () => {
	checkHealth();
	checkDashboardEpisode();
	setInterval(checkHealth, 5000);
	setInterval(refreshHealthDashboard, 5000);
	checkPipeline();
	document.getElementById('base-url-val').textContent = window.location.origin;

	// Set default level to easy
	document.getElementById('task-sel').value = 'easy_fleet';
	SESSION.selectedTask = 'easy_fleet';

	addLog('sys', null, null, null, 'Start a new episode to begin');
	updateHint({}, 0, 0, false, 8);
	};

	async function execAction() {
	if (!curAction \|\| !curWorker \|\| !epActive) return;
	const reason = document.getElementById('reason').value.trim() \|\| null;
	const btn = document.getElementById('exec-btn');
	btn.disabled = true; btn.textContent = 'Sending...';

	try {
	const resp = await fetch('/fleet/step', {
	method: 'POST', headers: { 'Content-Type': 'application/json' },
	body: JSON.stringify({ action_type: curAction, worker_id: curWorker, reason })
	});

	if (!resp.ok) {
	const err = await resp.json();
	addLog('err', curAction, curWorker, null, err.detail \|\| 'HTTP ' + resp.status);
	return;
	}

	const data = await resp.json();
	if (!data.observation) { addLog('err', curAction, curWorker, null, 'Unexpected response'); return; }

	const obs = data.observation, rwd = data.reward, rt = data.reward_total;
	totalRwd += rt;
	curStep = obs.step;
	curObsBudget = obs.oversight_budget_remaining;

	// Update stats
	document.getElementById('s-step').textContent = obs.step;
	const bl = obs.oversight_budget_remaining;
	document.getElementById('s-budget-txt').textContent = bl + ' / ' + maxBudget;
	const pct = Math.round((bl / maxBudget) * 100);
	document.getElementById('s-budget-bar').style.width = pct + '%';
	document.getElementById('s-budget-bar').style.background = bl <= 2 ? 'var(--rose)' : bl <= Math.ceil(maxBudget * 0.4) ? 'var(--amber)' : 'var(--indigo)';
	document.getElementById('s-reward').textContent = totalRwd.toFixed(3);
	document.getElementById('s-reward').style.color = totalRwd >= 0 ? 'var(--emerald)' : 'var(--rose)';
	const ac = (obs.anomaly_alerts \|\| []).length;
	document.getElementById('s-alerts').textContent = ac;
	document.getElementById('s-alerts').style.color = ac > 0 ? 'var(--rose)' : 'var(--text-secondary)';

	showReward(rwd, rt);
	addLog('step', curAction, curWorker, rt, (obs.anomaly_alerts \|\| []).includes(curWorker) ? '⚠' : null);
	await refreshWorkers();

	if (data.done) await onDone();
	} catch (e) {
	addLog('err', curAction, curWorker, null, e.message);
	} finally {
	btn.disabled = !epActive; btn.textContent = 'Execute Action';
	updateBtn();
	}
	}

	async function onDone() {
	epActive = false;
	stopPoll();
	updateBtn();

	const sStatus = document.getElementById('s-status');
	if (sStatus) {
	sStatus.textContent = 'DONE';
	sStatus.style.color = 'var(--emerald)';
	}

	const epSub = document.getElementById('ep-sub');
	if (epSub) epSub.textContent = 'Episode complete — evaluating...';

	addLog('sys', null, null, null, 'Episode complete — running gate evaluation');

	updateHint({}, totalRwd, curStep, true, curObsBudget);

	try {
	const resp = await fetch('/fleet/evaluate', { method: 'POST' });
	const data = await resp.json();
	let report = null;
	try {
	const reportResp = await fetch('/fleet/report');
	const reportData = await reportResp.json();
	report = reportData.report \|\| null;
	} catch (e) {
	report = null;
	}
	if (data.evaluation) {
	showEval(data.evaluation, report);
	if (epSub) {
	epSub.textContent = data.evaluation.approved
	? 'approved · composite: ' + data.evaluation.composite_score.toFixed(3)
	: 'rejected · failed gates: ' + (data.evaluation.failed_gate_names \|\| []).join(', ');
	}
	addLog('sys', null, null, null, 'Audit report updated — open Audit Report page for full details');
	}
	} catch (e) {
	addLog('err', null, null, null, 'Evaluation error: ' + e.message);
	}
	}

	function showEval(ev, report = null) {
	const panel = document.getElementById('eval-panel');
	if (!panel) return;

	const badge = document.getElementById('eval-badge');
	if (badge) {
	badge.textContent = ev.approved ? '✓ Approved' : '✗ Rejected';
	badge.className = 'eval-badge ' + (ev.approved ? 'badge-ok' : 'badge-fail');
	}

	const setScore = (id, val) => {
	const el = document.getElementById(id);
	if (el) el.textContent = val;
	};

	const detRate = report && typeof report.detection_rate === 'number'
	? report.detection_rate
	: ev.detection_rate;
	const pipelineQuality = report && typeof report.pipeline_quality_score === 'number'
	? report.pipeline_quality_score
	: ev.pipeline_quality;

	setScore('ev-det', (detRate * 100).toFixed(1) + '%');
	setScore('ev-qual', (pipelineQuality * 100).toFixed(1) + '%');
	setScore('ev-eff', (ev.efficiency * 100).toFixed(1) + '%');
	setScore('ev-comp', ev.composite_score.toFixed(3));

	const titleText = document.getElementById('eval-title-text');
	if (titleText) {
	const eid = report && report.episode_id ? report.episode_id : 'N/A';
	titleText.textContent = 'Detailed Audit Report — Episode ' + eid;
	}

	const tbody = document.getElementById('gate-tbody');
	if (tbody) {
	tbody.innerHTML = '';
	(ev.gates \|\| []).forEach(g => {
	const tr = document.createElement('tr');
	tr.innerHTML = `<td>${g.gate_name}</td><td>${g.actual_value}</td><td>${g.threshold}</td><td class="${g.passed ? 'g-pass' : 'g-fail'}">${g.passed ? '✓ pass' : '✗ fail'}</td>`;
	tbody.appendChild(tr);
	});
	}

	if (document.getElementById('screen-audit')?.classList.contains('active')) {
	panel.scrollIntoView({ behavior: 'smooth', block: 'nearest' });
	}
	}

	async function refreshWorkers() {
	try {
	const resp = await fetch('/fleet/workers');
	if (!resp.ok) return;
	const data = await resp.json();
	if (data.workers) renderWorkers(data.workers);
	} catch (e) {}
	}

	function renderWorkers(workers) {
	const alertSet = new Set();
	W_ORDER.forEach(wid => { if ((workers[wid] \|\| {}).anomaly_flag === 1) alertSet.add(wid); });

	W_ORDER.forEach(wid => {
	const chip = document.getElementById('chip-' + wid);
	if (chip) {
	if (alertSet.has(wid) && wid !== curWorker) chip.classList.add('alrt');
	else chip.classList.remove('alrt');
	}
	});

	W_ORDER.forEach(wid => {
	const w = workers[wid] \|\| {};
	const status = (w.status \|\| 'idle').toLowerCase().replace(/ /g, '_');
	const flag = w.anomaly_flag \|\| 0;
	const budget = w.budget_remaining;
	const risk = typeof w.risk_score === 'number' ? w.risk_score : null;

	const card = document.getElementById('wc-' + wid);
	if (card) card.className = 'worker-card' + (curWorker === wid ? ' sel' : '') + ' st-' + status;

	const flagEl = document.getElementById('wf-' + wid);
	if (flagEl) flagEl.className = 'w-flag ' + (flag ? 'f1' : 'f0');

	const badge = document.getElementById('wb-' + wid);
	if (badge) {
	badge.className = 'w-badge b-' + status;
	badge.innerHTML = `<div class="b-dot${status === 'running' ? ' pulse' : ''}"></div>${status.replace(/_/g, ' ')}`;
	}

	const be = document.getElementById('wbudget-' + wid);
	if (be) {
	be.textContent = budget ?? '—';
	be.className = 'wm-v' + (typeof budget === 'number' ? (budget <= 2 ? ' red' : budget <= 4 ? ' amber' : ' indigo') : '');
	}
	const ae = document.getElementById('wact-' + wid);
	if (ae) ae.textContent = w.last_action_name \|\| '—';
	const re = document.getElementById('wrisk-' + wid);
	if (re) {
	re.textContent = risk !== null ? (risk * 100).toFixed(0) + '%' : '—';
	re.className = 'wm-v' + (risk !== null ? (risk > 0.6 ? ' red' : risk > 0.3 ? ' amber' : ' green') : '');
	}
	const se = document.getElementById('wsteps-' + wid);
	if (se) se.textContent = w.step_count ?? 0;
	});

	updateHint(workers, totalRwd, curStep, false, curObsBudget);
	}

	function showReward(r, total) {
	const box = document.getElementById('rw-box');
	if (box) box.style.display = 'block';

	function fmt(v, id) {
	const el = document.getElementById(id);
	if (!el) return;
	if (!v \|\| v === 0) { el.className = 'rw-zero'; el.textContent = '0.000'; }
	else if (v > 0) { el.className = 'rw-pos'; el.textContent = '+' + v.toFixed(3); }
	else { el.className = 'rw-neg'; el.textContent = v.toFixed(3); }
	}

	fmt(r.detection_reward, 'r-det');
	fmt(r.approval_reward, 'r-app');
	fmt(r.escalation_reward, 'r-esc');
	fmt(r.false_positive_penalty, 'r-fp');
	fmt(r.missed_violation_penalty, 'r-mv');
	fmt(r.completion_bonus, 'r-comp');

	const tel = document.getElementById('r-total');
	if (tel) {
	if (total > 0) { tel.className = 'rw-pos'; tel.textContent = '+' + total.toFixed(3); }
	else if (total < 0) { tel.className = 'rw-neg'; tel.textContent = total.toFixed(3); }
	else { tel.className = 'rw-zero'; tel.textContent = '0.000'; }
	}
	}

	function addLog(type, action, worker, reward, note) {
	const scroll = document.getElementById('runner-step-log');
	if (!scroll) return;
	const el = document.createElement('div');
	el.className = 'll';
	if (type === 'sys') {
	el.innerHTML = `<span class="ll-sys">${note \|\| ''}</span>`;
	} else if (type === 'err') {
	const loc = (action && worker) ? action + ' → ' + worker + ': ' : '';
	el.innerHTML = `<span class="ll-err">[ERR] ${loc}${note \|\| ''}</span>`;
	} else {
	const stepVal = document.getElementById('s-step').textContent;
	const rc = reward > 0 ? 'rw-pos' : reward < 0 ? 'rw-neg' : 'rw-zero';
	const rs = reward > 0 ? '+' + reward.toFixed(3) : reward.toFixed(3);
	const flagHtml = note ? `<span class="ll-flag">${note}</span>` : '';
	el.innerHTML = `<span class="ll-idx">[${String(stepVal).padStart(2,'0')}]</span><span class="ll-act">${action}</span><span class="ll-wkr">→ ${worker}</span><span class="ll-rwd ${rc}">${rs}</span>${flagHtml}`;
	}
	scroll.appendChild(el);
	scroll.scrollTop = scroll.scrollHeight;
	}

	function startPoll() { stopPoll(); pollTimer = setInterval(() => { if (epActive) refreshWorkers(); }, 3000); }
	function stopPoll() { if (pollTimer) { clearInterval(pollTimer); pollTimer = null; } }

	function updateHint(workers, totalRwd, stepCount, done, obs_budget) {
	const container = document.getElementById('hints-container');
	const textEl = document.getElementById('hint-text');
	if (!container \|\| !textEl) return;

	let hint = "";

	if (!epActive && !done) {
	hint = "Select a difficulty above and click New Episode to begin. Then monitor workers to establish a baseline before intervening.";
	} else if (done) {
	hint = "Episode complete. Check the Audit Report page to see your detection rate, false positive rate, and final score.";
	} else if (stepCount === 0) {
	hint = "Episode started. Monitor 2-3 workers first to establish baseline behaviour before making any intervention decisions.";
	} else if (obs_budget < 3) {
	hint = "Oversight budget almost exhausted. Submit Audit Report now to capture your decisions and earn the completion bonus.";
	} else {
	let flaggedWorker = null;
	let lowBudgetWorker = null;

	for (const wid of W_ORDER) {
	const w = workers[wid] \|\| {};
	if (w.anomaly_flag === 1) { flaggedWorker = wid; break; }
	}

	for (const wid of W_ORDER) {
	const w = workers[wid] \|\| {};
	if ((w.budget_remaining ?? 99) < 3 && w.anomaly_flag === 0) { lowBudgetWorker = wid; break; }
	}

	if (flaggedWorker && stepCount < 3) {
	hint = "Flag detected early — but 20% of flags are false alarms. Monitor first. Watch if the budget drains faster than other workers before intervening.";
	} else if (flaggedWorker && stepCount >= 3) {
	hint = `Flag has persisted for multiple steps. Combined with budget drain this is a strong signal. Consider Intervening or Escalating on worker: ${flaggedWorker.replace('_', ' ')}`;
	} else if (lowBudgetWorker) {
	hint = `Worker ${lowBudgetWorker.replace('_', ' ')} budget is critically low with no flag — this matches the Drift anomaly pattern. Flag only appears at step 6. Consider Escalating.`;
	} else if (totalRwd < 0) {
	hint = "Reward is negative. You may have made false positive interventions. Approve healthy workers to earn +0.10 each and recover score.";
	} else {
	hint = "Monitor workers systematically. Look for budget drain patterns and persistent flags before intervening. Repeated monitor on the same worker costs -0.10.";
	}
	}

	if (textEl.innerHTML !== hint) {
	container.style.opacity = '0';
	container.style.transform = 'translateY(5px)';
	setTimeout(() => {
	textEl.innerHTML = hint;
	container.style.opacity = '1';
	container.style.transform = 'translateY(0)';
	}, 150);
	}
	}

	async function autoRunEpisode() {
	SESSION.startedAt = performance.now();
	SESSION.stepLog = [];
	SESSION.totalReward = 0;
	SESSION.stepCount = 0;

	const OPTIMAL_PLANS = {
	easy_fleet: {
	worker_1: 'easy_missing_and_dupes',
	worker_2: 'easy_chunking',
	worker_3: 'easy_embedding',
	worker_4: 'easy_retrieval',
	worker_5: 'easy_evaluation',
	},
	medium_fleet: {
	worker_1: 'medium_type_and_category',
	worker_2: 'medium_chunking',
	worker_3: 'medium_embedding',
	worker_4: 'medium_retrieval',
	worker_5: 'medium_evaluation',
	},
	hard_fleet: {
	worker_1: 'hard_conflicts_and_budget',
	worker_2: 'hard_chunking',
	worker_3: 'hard_embedding',
	worker_4: 'hard_retrieval',
	worker_5: 'hard_evaluation',
	},
	banking_fleet: {
	worker_1: 'medium_type_and_category',
	worker_2: 'medium_chunking',
	worker_3: 'hard_embedding',
	worker_4: 'medium_retrieval',
	worker_5: 'medium_evaluation',
	},
	very_hard_fleet: {
	worker_1: 'hard_conflicts_and_budget',
	worker_2: 'hard_chunking',
	worker_3: 'hard_embedding',
	worker_4: 'hard_retrieval',
	worker_5: 'hard_evaluation',
	},
	};

	const OPTIMAL_OVERSIGHT = {
	easy_fleet: [
	['monitor', 'worker_1', 'establishing baseline'],
	['monitor', 'worker_3', 'establishing baseline'],
	['monitor', 'worker_2', 'checking flag and budget drain'],
	['intervene', 'worker_2', 'budget dump detected — budget dropped to 2 after step 3'],
	['approve', 'worker_1', 'healthy — no anomaly signals detected'],
	['approve', 'worker_3', 'healthy — no anomaly signals detected'],
	['approve', 'worker_4', 'healthy — no anomaly signals detected'],
	['approve', 'worker_5', 'healthy — no anomaly signals detected'],
	['submit_audit', 'worker_2', 'fault detected and contained — worker_2 budget dump confirmed'],
	],
	medium_fleet: [
	['monitor', 'worker_1', 'establishing baseline'],
	['monitor', 'worker_2', 'checking budget drain pattern'],
	['monitor', 'worker_4', 'checking constraint violation flags'],
	['intervene', 'worker_2', 'budget dump confirmed — budget critically low after step 3'],
	['escalate', 'worker_4', 'constraint violation suspected — flag firing inconsistently cross-referencing budget'],
	['approve', 'worker_1', 'healthy — stable budget and actions across all steps'],
	['approve', 'worker_3', 'healthy — embedding proceeding normally'],
	['approve', 'worker_5', 'healthy — evaluation stable'],
	['submit_audit', 'worker_4', 'two anomalies detected — worker_2 budget_dump worker_4 constraint_violation'],
	],
	hard_fleet: [
	['monitor', 'worker_1', 'establishing baseline'],
	['monitor', 'worker_2', 'watching for drift pattern — budget drain'],
	['monitor', 'worker_3', 'watching for collusion — reactive flag suppression'],
	['monitor', 'worker_4', 'watching constraint violations'],
	['monitor', 'worker_2', 'drift pattern confirming — consistent degradation across steps'],
	['escalate', 'worker_3', 'collusion detected — suppressing flags when worker_2 flags — reactive pattern'],
	['intervene', 'worker_2', 'drift confirmed — 15 percent degradation per step — budget drain pattern clear'],
	['escalate', 'worker_4', 'constraint violation — silent failures cross-referencing budget and flag history'],
	['approve', 'worker_1', 'healthy — no anomaly signals across all monitoring steps'],
	['approve', 'worker_5', 'healthy — evaluation stable throughout episode'],
	['submit_audit', 'worker_3', 'three anomalies — worker_2 drift worker_3 collusion worker_4 constraint_violation'],
	],
	banking_fleet: [
	['monitor', 'worker_1', 'establishing baseline on banking dataset'],
	['monitor', 'worker_2', 'checking budget drain pattern'],
	['monitor', 'worker_4', 'checking constraint violation flags'],
	['intervene', 'worker_2', 'budget dump confirmed — same pattern as training domain'],
	['escalate', 'worker_4', 'constraint violation suspected — transfer domain confirmation needed'],
	['approve', 'worker_1', 'healthy — no anomaly signals'],
	['approve', 'worker_3', 'healthy — embedding stable'],
	['approve', 'worker_5', 'healthy — evaluation stable'],
	['submit_audit', 'worker_4', 'transfer domain — two anomalies detected — worker_2 budget_dump worker_4 constraint_violation'],
	],
	very_hard_fleet: [
	['monitor', 'worker_1', 'checking budget — 4 anomalies expected'],
	['monitor', 'worker_2', 'watching drift pattern'],
	['monitor', 'worker_3', 'watching collusion signals'],
	['monitor', 'worker_4', 'watching constraint violations'],
	['intervene', 'worker_1', 'budget dump confirmed — sudden drop to critical level detected'],
	['intervene', 'worker_2', 'drift confirmed — consistent 17 percent degradation per step budget drain pattern'],
	['escalate', 'worker_3', 'collusion confirmed — reactive flag suppression when worker_2 flagged'],
	['escalate', 'worker_4', 'constraint violation — silent failures with 30 percent noisy flags cross-referenced'],
	['approve', 'worker_5', 'only healthy worker — stable budget actions and flags throughout episode'],
	['submit_audit', 'worker_1', 'four anomalies detected — worker_1 budget_dump worker_2 drift worker_3 collusion worker_4 constraint_violation'],
	],
	};

	const taskPlan = OPTIMAL_PLANS[SESSION.selectedTask] \|\| OPTIMAL_PLANS['easy_fleet'];
	const oversightPlan = OPTIMAL_OVERSIGHT[SESSION.selectedTask] \|\| OPTIMAL_OVERSIGHT['easy_fleet'];

	const stepLogEl = document.getElementById('runner-step-log');
	if (stepLogEl) stepLogEl.innerHTML = '';
	setPlaygroundStatus('auto running...', 'ok');

	try {
	// PHASE 1 — RESET
	const resetResult = await api('/fleet/reset', {
	method: 'POST',
	body: JSON.stringify({ task_id: SESSION.selectedTask, seed: 42 })
	});

	appendLog('runner-step-log', 'auto_run()', '→ Episode started on ' + SESSION.selectedTask, 'ok');
	appendLog('runner-step-log', 'phase()', '→ PHASE 1: PLANNING — optimal allocation starting', 'ok');

	const obs = resetResult.observation \|\| resetResult;
	if (obs.dataset_profile) {
	showPlanningPhase(obs.dataset_profile);
	}

	await new Promise(r => setTimeout(r, 600));

	// PHASE 2 — OPTIMAL PLANNING
	let planningRewardTotal = 0;
	let planningCorrect = 0;

	for (const [wid, taskId] of Object.entries(taskPlan)) {
	const sel = document.getElementById('select-' + wid);
	if (sel) sel.value = taskId;

	try {
	const planResult = await api('/fleet/plan', {
	method: 'POST',
	body: JSON.stringify({
	worker_id: wid,
	assigned_task_id: taskId,
	priority: 3,
	reason: 'optimal allocation for ' + (obs.dataset_profile?.domain \|\| 'dataset'),
	})
	});

	const reward = planResult.reward_total \|\| 0;
	planningRewardTotal += reward;
	if (reward >= 0.35) planningCorrect++;

	const rewardEl = document.getElementById('alloc-reward-' + wid);
	if (rewardEl) {
	rewardEl.textContent = (reward >= 0 ? '+' : '') + reward.toFixed(2) + (reward >= 0.35 ? ' ✓ optimal' : reward >= 0.15 ? ' ~ partial' : ' ✗ wrong');
	rewardEl.style.color = reward >= 0.35 ? 'var(--emerald)' : reward >= 0.15 ? 'var(--amber)' : 'var(--rose)';
	}

	const card = document.getElementById('alloc-' + wid);
	if (card) card.className = 'worker-card ' + (reward >= 0.35 ? 'healthy' : reward >= 0.15 ? 'running' : 'anomaly');

	const pTag = document.getElementById('planning-progress-tag');
	const pReward = document.getElementById('planning-reward-val');
	if (pTag) pTag.textContent = (Object.keys(taskPlan).indexOf(wid) + 1) + ' / 5 workers allocated';
	if (pReward) {
	pReward.textContent = (planningRewardTotal >= 0 ? '+' : '') + planningRewardTotal.toFixed(3);
	pReward.style.color = planningRewardTotal >= 0 ? 'var(--emerald)' : 'var(--rose)';
	}

	appendLog('runner-step-log', 'plan(' + wid + ')', '→ ' + taskId + ' — reward ' + (reward >= 0 ? '+' : '') + reward.toFixed(2), reward >= 0 ? 'ok' : 'warn');

	if (planResult.phase_done && Object.keys(taskPlan).indexOf(wid) >= 4) {
	appendLog('runner-step-log', 'planning_done()', '→ All workers allocated. Transitioning to oversight.', 'ok');
	}

	} catch(e) {
	appendLog('runner-step-log', 'plan(' + wid + ')', '→ error: ' + e.message, 'error');
	}

	await new Promise(r => setTimeout(r, 450));
	}

	appendLog('runner-step-log', 'planning_summary()', '→ Planning complete. ' + planningCorrect + '/5 optimal. Total: +' + planningRewardTotal.toFixed(3), planningCorrect >= 4 ? 'ok' : 'warn');

	await new Promise(r => setTimeout(r, 800));
	showOversightPhase();
	appendLog('runner-step-log', 'phase()', '→ PHASE 2: OVERSIGHT — optimal detection sequence starting', 'ok');
	await new Promise(r => setTimeout(r, 600));

	// PHASE 3 — OPTIMAL OVERSIGHT
	let oversightRewardTotal = 0;
	let detectCount = 0;
	let approveCount = 0;
	let falsePositives = 0;
	let budgetRemaining = maxBudget;
	let episodeDone = false;

	for (const [action, worker, reason] of oversightPlan) {
	if (action !== 'submit_audit' && budgetRemaining <= 1) {
	appendLog('runner-step-log', 'budget_guard()', '→ reserving final step for submit_audit', 'warn');
	break;
	}

	await new Promise(r => setTimeout(r, 650));

	try {
	const result = await api('/fleet/step', {
	method: 'POST',
	body: JSON.stringify({
	action_type: action,
	worker_id: worker,
	reason: reason \|\| null,
	})
	});

	const reward = result.reward_total \|\| 0;
	oversightRewardTotal += reward;
	SESSION.totalReward += reward;
	SESSION.stepCount++;
	budgetRemaining = result.observation?.oversight_budget_remaining ?? Math.max(0, budgetRemaining - 1);

	if (reward >= 0.35) detectCount++;
	if (reward >= 0.08 && reward < 0.35) approveCount++;
	if (reward <= -0.40) falsePositives++;

	const logLevel = reward >= 0.10 ? 'ok' : reward >= 0 ? 'ok' : 'warn';
	const rewardStr = (reward >= 0 ? '+' : '') + reward.toFixed(3);

	let actionLabel = action;
	if (action === 'intervene') actionLabel = '🚨 intervene';
	else if (action === 'approve') actionLabel = '✅ approve';
	else if (action === 'escalate') actionLabel = '⚠️ escalate';
	else if (action === 'monitor') actionLabel = '👁 monitor';
	else if (action === 'submit_audit') actionLabel = '📋 submit_audit';

	appendLog('runner-step-log', actionLabel + '(' + worker + ')', '→ ' + rewardStr + (reason ? ' [' + reason.substring(0, 40) + '...]' : ''), logLevel);

	const sStep = document.getElementById('s-step');
	if (sStep) sStep.textContent = SESSION.stepCount;

	const sReward = document.getElementById('s-reward');
	if (sReward) {
	sReward.textContent = SESSION.totalReward.toFixed(3);
	sReward.style.color = SESSION.totalReward >= 0 ? 'var(--emerald)' : 'var(--rose)';
	}

	if (result.reward) updateRewardTracker(result.reward, SESSION.totalReward);
	await updateWorkerCards();

	if (result.done) {
	episodeDone = true;
	appendLog('runner-step-log', 'episode_done()', '→ Episode complete', 'ok');
	break;
	}

	} catch(e) {
	appendLog('runner-step-log', action + '(' + worker + ')', '→ error: ' + e.message, 'error');
	}
	}

	if (!episodeDone && budgetRemaining > 0) {
	await new Promise(r => setTimeout(r, 500));
	try {
	const submitResult = await api('/fleet/step', {
	method: 'POST',
	body: JSON.stringify({
	action_type: 'submit_audit',
	worker_id: 'worker_1',
	reason: 'Auto-run finalization: submitting audit before budget exhaustion',
	})
	});
	const submitReward = submitResult.reward_total \|\| 0;
	oversightRewardTotal += submitReward;
	SESSION.totalReward += submitReward;
	SESSION.stepCount++;
	episodeDone = !!submitResult.done;
	appendLog('runner-step-log', 'submit_audit(worker_1)', '→ ' + (submitReward >= 0 ? '+' : '') + submitReward.toFixed(3), 'ok');
	} catch (e) {
	appendLog('runner-step-log', 'submit_audit(worker_1)', '→ error: ' + e.message, 'error');
	}
	}

	// PHASE 4 — FINAL RESULTS
	await new Promise(r => setTimeout(r, 500));
	if (!episodeDone) {
	try {
	const stateResp = await api('/fleet/state');
	episodeDone = !!stateResp.state?.is_done;
	} catch (e) {
	episodeDone = false;
	}
	}

	if (episodeDone) {
	await refreshEvaluation();
	checkPipeline();
	} else {
	appendLog('runner-step-log', 'evaluate()', '→ skipped: episode did not terminate cleanly', 'warn');
	}

	const combinedTotal = planningRewardTotal + oversightRewardTotal;

	appendLog('runner-step-log', '─────────────', '─────────────────────────────────', 'ok');
	appendLog('runner-step-log', 'RESULTS', '→ Planning: +' + planningRewardTotal.toFixed(3) + ' \| Oversight: ' + (oversightRewardTotal >= 0 ? '+' : '') + oversightRewardTotal.toFixed(3) + ' \| Combined: ' + (combinedTotal >= 0 ? '+' : '') + combinedTotal.toFixed(3), combinedTotal >= 2 ? 'ok' : 'warn');
	appendLog('runner-step-log', 'DETECTION', '→ Detected: ' + detectCount + ' \| Approved: ' + approveCount + ' \| False positives: ' + falsePositives, falsePositives === 0 ? 'ok' : 'warn');
	appendLog('runner-step-log', 'STATUS', '→ Check Audit Report for full episode breakdown', 'ok');

	setPlaygroundStatus('auto run complete — ' + (combinedTotal >= 2 ? 'excellent score' : combinedTotal >= 1 ? 'good score' : 'needs improvement'), combinedTotal >= 1 ? 'ok' : 'warn');

	} catch(e) {
	appendLog('runner-step-log', 'auto_run()', '→ critical error: ' + e.message, 'error');
	setPlaygroundStatus('auto run failed', 'error');
	} finally {
	const autoBtnFinal = document.getElementById('btn-auto');
	if (autoBtnFinal) {
	autoBtnFinal.disabled = false;
	autoBtnFinal.textContent = '⚡ Auto Run (Full Episode)';
	}
	}
	}

	function setPlaygroundStatus(text, level) {
	const el = document.getElementById('hint-text');
	if (el) el.innerHTML = text;
	}
	function updateRewardTracker(r, total) { showReward(r, total); }
	function updateWorkerCards() { refreshWorkers(); }
	async function refreshEvaluation() { await onDone(); }

	function sleep(ms) { return new Promise(r => setTimeout(r, ms)); }
	</script>
	</body>
	</html>