RESUME.md

Resume material

Every number below is measured, with the command that produced it. Where a figure is a projection rather than a measurement, it says so — an interviewer who catches one inflated number stops believing all of them.

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Project title

Multi-Agent Research Orchestration System with MCP & HITL Governance (AlphaBrief) — Supervisor-pattern agent system (LangGraph) with an MCP tool server, deterministic numeric verification and human-approval gating — solving hours of manual daily research with zero tolerance for hallucinated numbers.

Tech stack line

LangGraph, MCP (Model Context Protocol), Claude Sonnet 4.6 + Haiku 4.5, FastAPI, yfinance, Next.js 15, Neon Postgres, Langfuse, Docker, GCP Cloud Run, Terraform, Vercel Cron

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Bullets

Automated two years' worth of my own manual morning research into an 8-second governed pipeline by architecting a supervisor-pattern multi-agent system (LangGraph, Claude Sonnet 4.6 / Haiku 4.5) in which parallel data, news and writer agents consume every tool through a Model Context Protocol server — 38 tool calls and 3 model calls per 5-ticker brief, merged race-free through typed state reducers.

Guaranteed 100% numeric accuracy across 20 evaluated runs — hallucinated figures are impossible by construction, not by prompting: all metrics are tool-computed, the brief schema rejects any prose containing a bare numeral, and a deterministic verification node recomputes all 35 claims per brief from raw price bars using an independent implementation before a human-in-the-loop gate (LangGraph interrupts + checkpointer) releases delivery.

Achieved 100% end-to-end run success with proven graceful degradation — invalid tickers, dead networks and empty feeds produce flagged partial briefs rather than crashes, validated by failure-injection tests that induce each fault for real (unknown symbol, unroutable proxy, genuinely empty feed) plus two adversarial eval controls that confirm the verifier fires rather than rubber-stamping.

Held infrastructure to $0 and enforced per-run model spend with a hard budget guard — Haiku-routed supervision, a 15-iteration cap and a token/USD ceiling that aborts with BUDGET_ABORT; race-free parallel state via reducer merges; step-level Langfuse tracing; GCP Cloud Run free tier provisioned with Terraform, Vercel Cron scheduling, Neon Postgres persistence.

Shorter variant (two bullets, for a one-page CV)

Built a governed multi-agent research system (LangGraph + MCP) that makes hallucinated numbers structurally impossible — every figure is tool-computed, independently recomputed by a deterministic verifier, and human-approved before delivery. 100% numeric accuracy across 20 evaluated runs; 190 tests; ruff, mypy --strict, eslint and tsc all zero-warning.

Shipped it end to end on free tiers for $0 — FastAPI + LangGraph + an MCP tool server in one container on GCP Cloud Run (Terraform-provisioned), a Next.js 15 console with live SSE agent telemetry, Vercel Cron scheduling, Neon Postgres and Langfuse tracing.

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Measured numbers

Figure	Value	How it was measured
Full 5-ticker brief, end to end	7.8 s	Cold provider cache, stdio MCP transport, AAPL/MSFT/NVDA/TSLA/AMZN
MCP tool calls per brief	38	Same run
Model calls per brief	3	Supervisor + data agent + news agent (deterministic writer)
Numeric claims verified per brief	35 / 35	Coverage 1.0
Telemetry events per brief	78	Streamed over SSE
Post-verification numeric accuracy	100.00%	make eval — 20 runs
End-to-end run success rate	100.0%	Same
Tool-call error rate	0.00%	Same
Supervisor iterations per brief	2	Fan-out, then route to writer
Tests	190	pytest -q
Quality gates	0 warnings	ruff, ruff-format, mypy --strict, pytest (filterwarnings=error), eslint, tsc, next build

Stated as a projection, not a measurement

Per-brief Claude spend ≈ $0.05–0.15. This is a projection from published Haiku 4.5 and Sonnet 4.6 pricing against the measured call pattern (3 model calls per brief), not a billed figure — the API key arrives after 2026-08-01. The enforced ceiling is real and tested: TOKEN_BUDGET_USD defaults to $0.50 and the run aborts with BUDGET_ABORT rather than exceed it.

Say it exactly that way in an interview. "Projected from list price against a measured call pattern; the hard ceiling is enforced and tested" is a stronger answer than a confident wrong number.

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Demo script (90 seconds)

0:00 — Press RUN. (25s) Live telemetry streams: the supervisor dispatching both workers in parallel, then each MCP tool call scrolling with its arguments and millisecond timing — get_price_history(ticker=AAPL, days=120) 967ms → 90 daily bars. Completeness bars fill per ticker.

"Two agents in one LangGraph superstep. Every number you're about to see comes from a tool call, not from a model."

0:25 — Verification. (15s) The ticks cascade green, claim by claim, each showing the stated value beside the value recomputed from the raw bars.

"Every number recomputed by code, not trusted from the model — and by a different implementation than the one that produced it."

0:40 — Approve. (15s) The graph is paused at a checkpointed interrupt. Approve; the brief renders and lands in email. Hold the phone up.

"It physically cannot deliver without this click."

0:55 — The kill shot. (20s) Switch to Fault mode and run again: a dead ticker. The run completes, the brief is explicitly marked partial, the gap is listed, everything else still verifies clean. Then Mismatch mode: a corrupted figure turns a row red, the writer regenerates once, and the run lands in HUMAN_REVIEW instead of being delivered.

"Production is what happens when things fail."

1:15 — Close. (15s)

"I did this job manually for two years. Now I govern the system that does it."

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Skills block (categorised, as ATS parsers prefer)

Languages: Python, TypeScript, SQL

GenAI / LLM: Claude (Anthropic API), Prompt Engineering, Structured Outputs (Tool Use), Model Routing, Hallucination Control, Context Management

Retrieval / RAG: RAG Pipelines, Vector Databases (Qdrant), Embeddings (bge/FastEmbed), Hybrid Retrieval (Dense + BM25), Reciprocal Rank Fusion, Cross-Encoder Reranking, Chunking Strategies, RAGAS Evaluation

Agentic AI: LangGraph, Multi-Agent Systems (Supervisor Pattern), MCP (Model Context Protocol), Tool Calling, Human-in-the-Loop, Agent Guardrails, Agent Evaluation

LLMOps / MLOps: Langfuse (Observability & Tracing), Evaluation Pipelines, Latency & Cost Optimization, CI Quality Gates (ruff, mypy, pytest)

Backend & Data: FastAPI, REST APIs, Pydantic, PostgreSQL (Neon), SQLAlchemy, pandas, Anomaly Detection, SSE Streaming

Cloud & Infra: GCP (Cloud Run), Docker, Vercel, Terraform (IaC), Neon Postgres Spaces, n8n, GitHub Actions

ATS terms this project earns honestly

Multi-Agent Systems · LangGraph · MCP (Model Context Protocol) · Agent Orchestration · Tool Calling · Human-in-the-Loop · Agent Guardrails · Agent Evaluation · State Management · Observability (Langfuse) · GCP Cloud Run · Terraform · Vercel Cron

Deliberately not claimed

PyTorch, TensorFlow, CUDA, LLM fine-tuning / LoRA, Kubernetes, distributed training. None are used here. A JD demanding them is an ML-researcher role — a different target.

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Questions this project lets you answer with a story

Question	The story
"Tell me about a bug that taught you something."	RunContext silently never reached graph nodes once a checkpointer was attached, because LangGraph filters unknown configurable keys — that dictionary is checkpoint state. Live handles belong in the runtime context channel. Found by running it, not by reading docs.
"How do you prevent hallucinated output?"	Three layers, in order: the model cannot type a number (schema), every number is minted from tool output (claim table), every number is recomputed from raw data by an independent implementation (verifier). Then a human.
"How do you know your tests are meaningful?"	The eval ships adversarial controls. A verifier that always passes proves nothing, so two runs deliberately break the system and assert the checks fire.
"What did you do about prompt injection?"	Headlines are third-party text: normalised, control-stripped, directive-neutralised, angle-bracket-stripped, length-capped, fenced in <untrusted_data>. Agents cannot widen their assigned ticker scope. A risk headline the model paraphrased is dropped unless it matches retrieved text verbatim. Eight-payload corpus in the tests.
"How would you make this production-grade?"	It has the shape already — caps, budget guard, DB-level concurrency constraint, structured degradation, tracing, an audit trail. The honest gaps are named in AUDIT.md: the SMTP send is unexercised, and no cloud deploy has been applied.