app/agentic/agents.py

"""Agentic RICS inspection pipeline.

These are *software agents* (modular components), not Cursor subagents.
The HeadAgent orchestrates retrieval + analysis steps and uses the existing
LLM adapter to draft final report sections in a consistent RICS tone.

Capabilities are defined in :mod:`app.agentic.tools`. When :func:`app.agentic.runtime_status.is_openai_inspector_live`
is true (non-empty ``OPENAI_API_KEY`` and ``inspector_tool_agent``), :mod:`app.agentic.inspector_loop` runs an OpenAI
**tool-calling** loop so the model chooses which retrieval / KB / duplicate-scan tools to invoke before
calling ``submit_inspection_section``. Otherwise (tests / no key) the legacy fixed pipeline runs. See ``GET /health`` → ``rics_inspector``.
"""

from __future__ import annotations

import asyncio
import json
import logging
from dataclasses import asdict
from typing import Any

from app.config import settings
from app.generator.postprocess import _L1_PLACEHOLDER, enforce_verify, strip_l1_advice
from app.models.schemas import SearchResult, WritingStyleProfile
from app.services.generation import _ai_level_to_params  # internal mapping
from app.services.provenance_enrichment import fetch_doc_filenames
from app.templates.registry import get_template, section_order_for_survey

from . import tools as agent_tools
from .inspector_loop import _verify_risks, run_inspector_tool_loop
from .models import ComplianceNote, EvidenceItem, Finding, RiskItem, StructuredReport
from .runtime_status import is_openai_inspector_live


def _safe_level(survey_level: int | None) -> int:
    """Coerce ``survey_level`` to a numeric tier, defaulting to L3 on bad input."""
    try:
        return int(survey_level if survey_level is not None else 3)
    except Exception:  # noqa: BLE001
        return 3

logger = logging.getLogger(__name__)


def _inspector_bundle(rep: StructuredReport) -> dict[str, Any] | None:
    """Subset of inspector artifacts safe for JSON responses."""
    out: dict[str, Any] = {}
    if rep.extraction_audit:
        out["extraction_audit"] = rep.extraction_audit
    if rep.section_plan:
        out["section_plan"] = rep.section_plan
    if rep.condition_rating_summary:
        out["condition_rating_summary"] = rep.condition_rating_summary
    if rep.tool_trace:
        out["tool_trace"] = list(rep.tool_trace)[-48:]
    return out or None


class DataExtractionAgent:
    """Retrieve relevant evidence for each report element."""

    async def gather_async(
        self,
        *,
        tenant_id: str,
        primary_document_id: str,
        section_code: str,
        bullets: list[str],
        reference_document_ids: list[str] | None = None,
        kb_enabled: bool = True,
        retrieval_level: str = "paragraph",
    ) -> list[SearchResult]:
        query = " ".join([b for b in bullets if b.strip()])
        refs = list(reference_document_ids or [])
        out: list[SearchResult] = []
        out.extend(
            await agent_tools.retrieve_tenant_evidence_async(
                query=query,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                secondary_document_ids=refs,
                k=max(settings.retrieval_top_k, 12),
                rerank_top_n=min(6, settings.rerank_top_n + 3),
            )
        )
        if kb_enabled:
            hl = retrieval_level if retrieval_level in ("document", "section", "paragraph") else None
            out.extend(
                await agent_tools.retrieve_kb_guidance_async(
                    query=query,
                    k=10,
                    hierarchy_level=hl,
                    rerank_top_n=5,
                )
            )
        return agent_tools.dedupe_search_results(out)

    def gather(
        self,
        *,
        tenant_id: str,
        primary_document_id: str,
        section_code: str,
        bullets: list[str],
        reference_document_ids: list[str] | None = None,
        kb_enabled: bool = True,
        retrieval_level: str = "paragraph",
    ) -> list[SearchResult]:
        """Sync retrieval (tests / blocking contexts). Prefer :meth:`gather_async` in async handlers."""
        query = " ".join([b for b in bullets if b.strip()])
        refs = list(reference_document_ids or [])
        out: list[SearchResult] = []
        out.extend(
            agent_tools.retrieve_tenant_evidence(
                query=query,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                secondary_document_ids=refs,
                k=max(settings.retrieval_top_k, 12),
                rerank_top_n=min(6, settings.rerank_top_n + 3),
            )
        )
        if kb_enabled:
            hl = retrieval_level if retrieval_level in ("document", "section", "paragraph") else None
            out.extend(
                agent_tools.retrieve_kb_guidance(
                    query=query,
                    k=10,
                    hierarchy_level=hl,
                    rerank_top_n=5,
                )
            )
        return agent_tools.dedupe_search_results(out)


class StandardsComplianceAgent:
    """Check for typical RICS report hygiene and missing essentials."""

    def check(
        self,
        *,
        section_code: str,
        bullets: list[str],
        kb_guidance: list[str] | None = None,
        survey_level: int | None = None,
    ) -> list[ComplianceNote]:
        template = get_template(section_code, survey_level)
        expected = template.expected_fields if template else []
        raw = " ".join(bullets).lower()

        notes: list[ComplianceNote] = []
        for f in expected[:10]:  # cap to avoid noise
            ok = f.replace("_", " ") in raw or f.lower() in raw
            notes.append(
                ComplianceNote(
                    standard="RICS Home Survey Standard (structure hygiene)",
                    note=f"Expected field '{f}' appears in notes: {'yes' if ok else 'no'}",
                    status="OK" if ok else "Review",
                )
            )
        if kb_guidance:
            notes.append(
                ComplianceNote(
                    standard="Local KB (RICS/exemplar guidance)",
                    note=f"Retrieved {len(kb_guidance)} guidance snippet(s) relevant to {section_code}.",
                    status="OK",
                )
            )
        return notes


class RiskAssessmentAgent:
    """Translate findings into risk items using LLM-based contextual reasoning.

    Falls back to keyword heuristics when no OpenAI key is configured (e.g. tests).
    """

    _SYSTEM = (
        "You are a Chartered Building Surveyor (MRICS) assessing inspection notes.\n"
        "Return ONLY a JSON array of risk objects. Each object must have exactly these keys:\n"
        "  category (string), risk (string), severity (\"Low\"|\"Medium\"|\"High\"|\"Critical\"),\n"
        "  likelihood (\"Low\"|\"Medium\"|\"High\"), action (string).\n"
        "Rules:\n"
        "- Maximum 5 items.\n"
        "- Base severity ONLY on what the notes explicitly state — do NOT invent defects.\n"
        "- If notes say \"no signs of X\", \"satisfactory\", or \"good condition\", do NOT flag X.\n"
        "- If no defects are mentioned, return exactly one item: category \"General\", severity \"Low\", likelihood \"Low\".\n"
        "Output ONLY the JSON array, no markdown fences, no commentary."
    )

    # Keys the frozen RiskItem dataclass accepts via kwargs. The LLM contract above
    # asks for exactly these five — but models drift and routinely add extras like
    # "description", "details", "reasoning", or "evidence". Passing those straight
    # into RiskItem(**item) raises TypeError, which the broad except below would
    # silently swallow and downgrade the entire batch to crude keyword heuristics.
    # We keep `evidence` out of this whitelist on purpose: it's a structural
    # tuple[EvidenceItem, ...] field that the model can't populate correctly.
    _RISK_ITEM_FIELDS: frozenset[str] = frozenset(
        ("category", "risk", "severity", "likelihood", "action")
    )

    @classmethod
    def _coerce_risk_item(cls, raw: Any) -> RiskItem | None:
        """Build a RiskItem from one LLM-emitted dict, tolerant of drift.

        - Unknown keys are dropped (so e.g. an extra ``"description"`` is silently
          ignored instead of nuking the whole batch).
        - A single item missing a required key is skipped, not fatal — the rest
          of the batch survives.
        - Returns ``None`` if the raw value is unusable.
        """
        if not isinstance(raw, dict):
            return None
        clean: dict[str, str] = {}
        for k, v in raw.items():
            if k in cls._RISK_ITEM_FIELDS:
                clean[k] = "" if v is None else str(v).strip()
        if not cls._RISK_ITEM_FIELDS.issubset(clean):
            missing = sorted(cls._RISK_ITEM_FIELDS - set(clean))
            logger.warning(
                "LLM risk item dropped — missing required key(s) %s: %r",
                missing, raw,
            )
            return None
        return RiskItem(**clean)

    async def assess(self, *, section_code: str, bullets: list[str]) -> list[RiskItem]:
        from app.config import settings

        if not settings.openai_api_key:
            return self._keyword_fallback(bullets)

        notes = "\n".join(f"- {b}" for b in bullets if b.strip()) or "No notes provided."

        try:
            from app.llm.openai_chat import chat_completions_create

            user_content = f"Section: {section_code}\n\nInspection notes:\n{notes}"
            raw = await chat_completions_create(
                messages=[
                    {"role": "system", "content": self._SYSTEM},
                    {"role": "user", "content": user_content},
                ],
                model=settings.chat_model,
                max_tokens=600,
                temperature=0.0,
                phase="risk_assessment",
                section_id=section_code,
            )
            items = json.loads(raw)
            if not isinstance(items, list):
                raise ValueError("Expected JSON array")
            parsed = [
                item for item in (self._coerce_risk_item(it) for it in items[:5])
                if item is not None
            ]
            if not parsed:
                logger.warning(
                    "LLM returned %d risk item(s) but none parsed cleanly — using keyword fallback",
                    len(items),
                )
                return self._keyword_fallback(bullets)
            return parsed

        except Exception as exc:
            logger.warning("LLM risk assessment failed (%s) — using keyword fallback", exc)
            return self._keyword_fallback(bullets)

    def _keyword_fallback(self, bullets: list[str]) -> list[RiskItem]:
        """Simple keyword fallback used when OpenAI is unavailable."""
        text = " ".join(bullets).lower()
        risks: list[RiskItem] = []

        def add(cat: str, risk: str, sev: str, lik: str, action: str) -> None:
            risks.append(RiskItem(category=cat, risk=risk, severity=sev, likelihood=lik, action=action))

        if any(k in text for k in ("damp", "mould", "penetrating", "rising")):
            add("Moisture", "Moisture ingress — investigation required", "Medium", "Medium", "Investigate source; carry out repairs and monitor.")
        if any(k in text for k in ("crack", "movement", "subsidence", "bulging")):
            add("Structural", "Structural movement requiring specialist review", "High", "Medium", "Seek structural engineer review before commitment.")
        if any(k in text for k in ("electrical", "consumer unit", "rcd", "wiring", "fuse")):
            add("Electrical", "Electrical safety compliance uncertain", "High", "Medium", "Obtain EICR by a qualified electrician.")
        if any(k in text for k in ("gas", "boiler", "flue", "carbon monoxide")):
            add("Gas", "Gas safety — appliances require certification", "High", "Low", "Obtain Gas Safe service and flue test.")
        if not risks:
            add("General", "No significant defects identified in inspection notes", "Low", "Low", "Maintain property and address minor defects as they arise.")
        return risks


class ReportStructuringAgent:
    """Convert artifacts to a clean RICS narrative outline."""

    def outline(
        self,
        *,
        section_code: str,
        bullets: list[str],
        evidence_snippets: list[str],
        compliance: list[ComplianceNote],
        risks: list[RiskItem],
        survey_level: int | None = None,
    ) -> dict[str, Any]:
        template = get_template(section_code, survey_level)
        sk = template.skeleton if template else f"[{section_code}]: [content]."
        return {
            "section_code": section_code,
            "section_title": (template.title if template else section_code),
            "skeleton": sk,
            "bullets": bullets,
            "evidence": evidence_snippets[:10],
            "compliance": [asdict(x) for x in compliance][:10],
            "risks": [asdict(x) for x in risks][:10],
        }


def render_report_text(
    *,
    title: str,
    blocks: dict[str, str],
    section_code: str | None = None,
    survey_level: int | None = None,
) -> str:
    """Render report text for one section.

    For Level 3 element sections (outside/inside/services/grounds), real RICS PDFs
    use a more fluid narrative rather than repeating fixed subheadings per element.
    """
    code = (section_code or "").strip().upper()
    lvl = int(survey_level) if survey_level is not None else None

    # L3 narrative style for core element sections (E/F/G/H): cohesive paragraph(s), no nested subheadings.
    if lvl == 3 and code and code[0] in ("E", "F", "G", "H") and any(ch.isdigit() for ch in code[1:]):
        # Prefer condition assessment as the spine, then append risks/recs only if present.
        parts: list[str] = []
        ca = (blocks.get("Condition Assessment") or "").strip()
        dr = (blocks.get("Defects and Risks") or "").strip()
        rec = (blocks.get("Recommendations") or "").strip()
        if ca:
            parts.append(ca)
        if dr and (not ca or dr.lower() not in ca.lower()):
            parts.append(dr)
        if rec and (rec.lower() not in " ".join(parts).lower()):
            parts.append(rec)
        out = "\n\n".join([p for p in parts if p]).strip()
        return out or (blocks.get("Executive Summary") or "").strip() or title.strip()

    # Default headed style (kept for A–D, I–L and non-L3 packs).
    parts2: list[str] = [title.strip()]
    for h in ("Executive Summary", "Property Description", "Condition Assessment", "Defects and Risks", "Recommendations"):
        body = (blocks.get(h) or "").strip()
        if not body:
            continue
        parts2.append(f"\n\n{h}\n{body}")
    return "\n".join(parts2).strip()


def _dedupe_risks(items: list[RiskItem]) -> list[RiskItem]:
    seen: set[tuple[str, str]] = set()
    out: list[RiskItem] = []
    for r in items:
        k = (str(r.category).strip().lower(), str(r.risk).strip().lower())
        if k in seen:
            continue
        seen.add(k)
        out.append(r)
    return out


# Severity ordering MUST match the LLM prompt contract in `RiskAssessmentAgent._SYSTEM`,
# which currently allows {Critical, High, Medium, Low}. Lower sort-key = higher priority,
# so Critical comes first and any unrecognised value falls to the bottom (instead of being
# silently treated as more important than Low — the previous behaviour). Likelihood per
# the contract is {Low, Medium, High} only — no Critical there.
_SEVERITY_ORDER: dict[str, int] = {"critical": 0, "high": 1, "medium": 2, "low": 3}
_LIKELIHOOD_ORDER: dict[str, int] = {"high": 0, "medium": 1, "low": 2}


def _risk_priority_key(r: RiskItem) -> tuple[int, int]:
    sev = _SEVERITY_ORDER.get(str(r.severity).lower(), len(_SEVERITY_ORDER))
    lik = _LIKELIHOOD_ORDER.get(str(r.likelihood).lower(), len(_LIKELIHOOD_ORDER))
    return sev, lik


def render_full_report_text(
    *,
    title: str,
    executive_summary: str,
    sections: list[tuple[str, str, str]],
    consolidated_risks: list[RiskItem],
    recommendations: list[str],
) -> str:
    """Render a single end-to-end report text with headings."""
    parts: list[str] = [title.strip()]

    if executive_summary.strip():
        parts.append(f"\n\nExecutive Summary\n{executive_summary.strip()}")

    if consolidated_risks:
        lines = []
        for i, r in enumerate(consolidated_risks[:20], 1):
            lines.append(
                f"{i}. [{r.category}] {r.risk} (Severity: {r.severity}, Likelihood: {r.likelihood}) — {r.action}"
            )
        parts.append("\n\nDefects and Risks (consolidated)\n" + "\n".join(lines))

    if recommendations:
        rec_lines = []
        for i, t in enumerate([x for x in recommendations if x.strip()][:20], 1):
            rec_lines.append(f"{i}. {t.strip()}")
        parts.append("\n\nRecommendations (summary)\n" + "\n".join(rec_lines))

    # Per-section detail
    if sections:
        parts.append("\n\nCondition Assessment (by section)")
        for code, sec_title, sec_text in sections:
            body = (sec_text or "").strip()
            if not body:
                continue
            parts.append(f"\n\n{code} — {sec_title}\n{body}")

    return "\n".join(parts).strip()


class HeadAgent:
    """Professional RICS inspector orchestrator."""

    def __init__(self) -> None:
        self.extractor = DataExtractionAgent()
        self.compliance = StandardsComplianceAgent()
        self.risk = RiskAssessmentAgent()
        self.structurer = ReportStructuringAgent()

    async def generate_section_report(
        self,
        *,
        db,
        tenant_id: str,
        primary_document_id: str,
        section_code: str,
        bullets: list[str],
        style_profile: WritingStyleProfile,
        ai_percent: int = 50,
        retrieval_level: str = "paragraph",
        reference_document_ids: list[str] | None = None,
        similarity_scan: bool = False,
        peer_sections: dict[str, str] | None = None,
        similarity_exclude_document_ids: list[str] | None = None,
        survey_level: int | None = None,
        report_id: str | None = None,
    ) -> StructuredReport:
        section_bullets = list(bullets)
        if report_id:
            from app.services.photo_vision import enrich_bullets_with_section_photos

            section_bullets = await enrich_bullets_with_section_photos(
                db,
                tenant_id=tenant_id,
                report_id=str(report_id),
                section_code=section_code,
                bullets=section_bullets,
                survey_level=survey_level,
            )
        if is_openai_inspector_live():
            rep, _ = await run_inspector_tool_loop(
                db=db,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                section_code=section_code,
                bullets=section_bullets,
                style_profile=style_profile,
                ai_percent=ai_percent,
                retrieval_level=retrieval_level,
                reference_document_ids=list(reference_document_ids or []),
                peer_sections=dict(peer_sections or {}),
                survey_level=survey_level,
            )
            return rep

        # Legacy fixed pipeline (mock adapter, or inspector disabled)
        # 1) gather evidence (tenant + knowledge base)
        hits = await self.extractor.gather_async(
            tenant_id=tenant_id,
            primary_document_id=primary_document_id,
            section_code=section_code,
            bullets=section_bullets,
            reference_document_ids=reference_document_ids,
            kb_enabled=True,
            retrieval_level=retrieval_level,
        )

        # 2) enrich evidence items with filenames (tenant docs) + kb source labels
        tenant_doc_ids = {r.doc_id for r in hits if r.tenant_id == tenant_id and r.doc_id}
        filenames = await fetch_doc_filenames(db, tenant_id, tenant_doc_ids)
        evidence_items: list[EvidenceItem] = []
        snippets: list[str] = []
        kb_guidance: list[str] = []
        for r in hits:
            is_kb = bool(getattr(r, "kb", False)) or r.tenant_id == settings.knowledge_base_tenant_id
            src = (getattr(r, "source", None) or None) if is_kb else filenames.get(r.doc_id)
            if not src and is_kb:
                src = "Local RICS knowledge base"
            evidence_items.append(
                EvidenceItem(
                    doc_id=r.doc_id,
                    chunk_id=r.chunk_id,
                    score=float(r.score),
                    text=r.text,
                    source=src,
                    section_hint=getattr(r, "section_title", None),
                    kb=is_kb,
                )
            )
            snippets.append(r.text)
            if is_kb:
                kb_guidance.append(r.text)

        # 3) compliance + risk
        comp = self.compliance.check(
            section_code=section_code,
            bullets=section_bullets,
            kb_guidance=kb_guidance[:3],
            survey_level=survey_level,
        )
        risks = await self.risk.assess(section_code=section_code, bullets=section_bullets)

        if similarity_scan and db is not None:
            query = " ".join([b for b in section_bullets if b.strip()])
            try:
                sim = await agent_tools.find_similar_library_and_peers(
                    db,
                    tenant_id,
                    text=query or section_code,
                    section_code=section_code,
                    peer_sections=dict(peer_sections or {}),
                    exclude_document_ids=list(similarity_exclude_document_ids or []),
                )
                n_lib = len(sim.library_matches)
                n_peer = len(sim.draft_overlaps)
                if n_lib or n_peer:
                    comp = list(comp) + [
                        ComplianceNote(
                            standard="Corpus hygiene (find_similar_library_and_peers tool)",
                            note=(
                                f"Similarity scan: {n_lib} indexed library match(es), "
                                f"{n_peer} draft overlap(s) with peer sections. "
                                "Reconcile duplicates before sign-off."
                            ),
                            status="Review" if n_peer else "OK",
                        )
                    ]
            except Exception:
                logger.exception("Agent tool find_similar_library_and_peers failed for section=%s", section_code)

        # 4) structure prompt variables
        outline = self.structurer.outline(
            section_code=section_code,
            bullets=section_bullets,
            evidence_snippets=snippets,
            compliance=comp,
            risks=risks,
            survey_level=survey_level,
        )

        from app.llm import generation_facade as gen_llm

        ai_params = _ai_level_to_params(3, ai_percent=ai_percent)
        skeleton = outline["skeleton"]
        doc_ctx = outline["evidence"][:3]
        para_ctx = outline["evidence"][3:]
        base = await gen_llm.generate_section(
            skeleton=skeleton,
            bullets=section_bullets,
            snippets=[],
            style_profile=style_profile if ai_percent > 5 else None,
            temperature=float(ai_params["temperature"]),
            creativity_hint=str(ai_params["creativity_hint"])
            + "\n\nAGENTIC CONTEXT: You are drafting as a Chartered Building Surveyor (MRICS). "
            "Be risk-based and recommendation-led.",
            document_context=doc_ctx,
            hierarchy_section_snippets=None,
            paragraph_snippets=para_ctx,
            style_anchor=None,
            survey_level=survey_level,
            tenant_id=tenant_id,
        )

        # Build a section-scoped structured report (full-report assembly is
        # handled by API layer). The legacy non-agentic path runs only when
        # `is_openai_inspector_live()` is false (no key, or the agentic flag
        # is off — typically tests / offline). It still emits LLM-generated
        # text via the adapter, so we mirror the same regex non-invention
        # guard the agentic path applies to its risks. The fast regex pass
        # catches the common offenders (postcodes, addresses, named persons)
        # without adding the latency / cost of an LLM grounding round-trip
        # in a path that's mostly hit when no key is configured anyway.
        title = outline["section_title"]
        verified_base = enforce_verify(text=base, bullets=section_bullets, snippets=snippets)
        risks = _verify_risks(risks, bullets=section_bullets, snippets=snippets)

        # Tier-aware behavioural enforcement (parity with inspector_loop).
        # The legacy path runs in tests / no-key offline environments — but
        # also as the real production path when the inspector flag is off.
        # Either way an L1 product must read as observation, never advice.
        legacy_lvl = _safe_level(survey_level)
        if legacy_lvl <= 1:
            verified_base = strip_l1_advice(verified_base)
            risks = [
                RiskItem(
                    category=r.category,
                    risk=r.risk,
                    severity=r.severity,
                    likelihood=r.likelihood,
                    action=strip_l1_advice(r.action) if r.action else r.action,
                    evidence=r.evidence,
                )
                for r in risks
            ]
            defects = " ".join(
                f"{r.category}: {r.risk} ({r.severity}/{r.likelihood})."
                for r in risks
            ).strip()[:2400]
            recs = _L1_PLACEHOLDER
        else:
            defects = " ".join(
                f"{r.category}: {r.risk} ({r.severity}/{r.likelihood}). {r.action}"
                for r in risks
            ).strip()[:2400]
            recs = " ".join(r.action for r in risks).strip()[:1600]

        # Previous behaviour set both `property_description` and
        # `condition_assessment` to the same `base[:900]`, plus a third
        # `executive_summary = title: base` containing yet another copy of
        # the text. That's three views of the same truncated string —
        # exactly the "summary feel" the user reported. Place the full
        # verified draft once, in `condition_assessment`, and leave
        # `property_description` empty so the renderer skips it rather than
        # repeating itself. `executive_summary` becomes a short MRICS-style
        # headline rather than a third copy of the body.
        return StructuredReport(
            executive_summary=f"{title} — Chartered Building Surveyor inspection summary.",
            property_description="",
            condition_assessment=verified_base.strip(),
            defects_and_risks=defects,
            recommendations=recs,
            findings=(),
            risks=tuple(risks),
            compliance=tuple(comp),
            evidence_items=tuple(evidence_items),
            tool_trace=(),
        )


async def generate_full_report(
    *,
    db,
    tenant_id: str,
    primary_document_id: str,
    bullets_by_section: dict[str, list[str]],
    style_profile: WritingStyleProfile,
    ai_percent: int = 50,
    retrieval_level: str = "paragraph",
    reference_document_ids: list[str] | None = None,
    similarity_scan: bool = False,
    peer_sections: dict[str, str] | None = None,
    similarity_exclude_document_ids: list[str] | None = None,
    survey_level: int | None = None,
    report_id: str | None = None,
) -> dict[str, Any]:
    """Generate a full multi-section report as a JSON structure + stitched report text."""
    head = HeadAgent()
    out: dict[str, Any] = {"sections": {}}
    all_risks: list[RiskItem] = []
    section_detail_for_stitch: list[tuple[str, str, str]] = []
    recs: list[str] = []
    codes = section_order_for_survey(survey_level)

    def _missing_placeholder(sec_code: str) -> tuple[str, str]:
        template = get_template(sec_code, survey_level)
        sec_title_local = template.title if template else sec_code
        if template and template.has_condition_rating:
            missing = "Not inspected. Condition Rating NI."
        else:
            missing = "Not applicable."
        return sec_title_local, missing

    from app.db.database import multi_section_parallel_enabled

    # Parallel section generation when async pipeline and DB backend allow it.
    if multi_section_parallel_enabled():
        import structlog

        log = structlog.get_logger(__name__)

        tasks: list[tuple[str, asyncio.Task[Any]]] = []
        for code in codes:
            bullets = bullets_by_section.get(code) or []
            if not bullets:
                sec_title, missing = _missing_placeholder(code)
                out["sections"][code] = {
                    "executive_summary": missing,
                    "property_description": missing,
                    "condition_assessment": missing,
                    "defects_and_risks": missing,
                    "recommendations": missing,
                    "report_text": render_report_text(
                        title=f"RICS Inspection Report — Section {code}",
                        blocks={
                            "Executive Summary": missing,
                            "Property Description": missing,
                            "Condition Assessment": missing,
                            "Defects and Risks": missing,
                            "Recommendations": missing,
                        },
                        section_code=code,
                        survey_level=survey_level,
                    ),
                    "risks": [],
                    "compliance": [],
                    "evidence_items": [],
                    "tool_trace": [],
                    "inspector": None,
                }
                section_detail_for_stitch.append((code, sec_title, missing))
                continue

            async def _run_one_section(
                sec_code: str,
                sec_bullets: list[str],
                sec_report_id: str | None,
            ) -> Any:
                from app.db.database import get_session_factory

                factory = get_session_factory()
                async with factory() as section_db:
                    return await head.generate_section_report(
                        db=section_db,
                        tenant_id=tenant_id,
                        primary_document_id=primary_document_id,
                        section_code=sec_code,
                        bullets=sec_bullets,
                        style_profile=style_profile,
                        ai_percent=ai_percent,
                        retrieval_level=retrieval_level,
                        reference_document_ids=reference_document_ids,
                        similarity_scan=similarity_scan,
                        peer_sections=peer_sections,
                        similarity_exclude_document_ids=similarity_exclude_document_ids,
                        survey_level=survey_level,
                        report_id=sec_report_id,
                    )

            tasks.append(
                (
                    code,
                    asyncio.create_task(_run_one_section(code, bullets, report_id)),
                )
            )

        if tasks:
            results = await asyncio.gather(
                *(t for _, t in tasks), return_exceptions=True
            )
            for (code, _task), rep in zip(tasks, results):
                if isinstance(rep, BaseException):
                    log.error(
                        "section_generation_failed",
                        event="section_generation_failed",
                        phase="generate_full_report",
                        section_id=code,
                        cache_hit=None,
                        exc_type=type(rep).__name__,
                        error=str(rep),
                    )
                    sec_title, missing = _missing_placeholder(code)
                    missing_detail = "Section generation failed; verify inputs and regenerate."
                    out["sections"][code] = {
                        "executive_summary": missing_detail,
                        "property_description": missing_detail,
                        "condition_assessment": missing_detail,
                        "defects_and_risks": missing_detail,
                        "recommendations": missing_detail,
                        "report_text": render_report_text(
                            title=f"RICS Inspection Report — Section {code}",
                            blocks={
                                "Executive Summary": missing_detail,
                                "Property Description": missing_detail,
                                "Condition Assessment": missing_detail,
                                "Defects and Risks": missing_detail,
                                "Recommendations": missing_detail,
                            },
                            section_code=code,
                            survey_level=survey_level,
                        ),
                        "risks": [],
                        "compliance": [],
                        "evidence_items": [],
                        "tool_trace": [],
                        "inspector": None,
                    }
                    section_detail_for_stitch.append(
                        (code, sec_title, missing)
                    )
                    continue

                inspector = _inspector_bundle(rep)
                out["sections"][code] = {
                    "executive_summary": rep.executive_summary,
                    "property_description": rep.property_description,
                    "condition_assessment": rep.condition_assessment,
                    "defects_and_risks": rep.defects_and_risks,
                    "recommendations": rep.recommendations,
                    "report_text": render_report_text(
                        title=f"RICS Inspection Report — Section {code}",
                        blocks={
                            "Executive Summary": rep.executive_summary,
                            "Property Description": rep.property_description,
                            "Condition Assessment": rep.condition_assessment,
                            "Defects and Risks": rep.defects_and_risks,
                            "Recommendations": rep.recommendations,
                        },
                        section_code=code,
                        survey_level=survey_level,
                    ),
                    "risks": [asdict(r) for r in rep.risks],
                    "compliance": [asdict(c) for c in rep.compliance],
                    "evidence_items": [asdict(e) for e in rep.evidence_items],
                    "tool_trace": list(rep.tool_trace),
                    "inspector": inspector,
                }

                all_risks.extend(list(rep.risks))
                template = get_template(code, survey_level)
                sec_title = template.title if template else code
                section_detail_for_stitch.append(
                    (
                        code,
                        sec_title,
                        rep.condition_assessment
                        or rep.property_description
                        or "",
                    )
                )
                if rep.recommendations:
                    recs.append(rep.recommendations)

    else:
        # Legacy sequential generation.
        for code in codes:
            bullets = bullets_by_section.get(code) or []
            if not bullets:
                sec_title, missing = _missing_placeholder(code)
                out["sections"][code] = {
                    "executive_summary": missing,
                    "property_description": missing,
                    "condition_assessment": missing,
                    "defects_and_risks": missing,
                    "recommendations": missing,
                    "report_text": render_report_text(
                        title=f"RICS Inspection Report — Section {code}",
                        blocks={
                            "Executive Summary": missing,
                            "Property Description": missing,
                            "Condition Assessment": missing,
                            "Defects and Risks": missing,
                            "Recommendations": missing,
                        },
                        section_code=code,
                        survey_level=survey_level,
                    ),
                    "risks": [],
                    "compliance": [],
                    "evidence_items": [],
                    "tool_trace": [],
                    "inspector": None,
                }
                section_detail_for_stitch.append((code, sec_title, missing))
                continue

            rep = await head.generate_section_report(
                db=db,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                section_code=code,
                bullets=bullets,
                style_profile=style_profile,
                ai_percent=ai_percent,
                retrieval_level=retrieval_level,
                reference_document_ids=reference_document_ids,
                similarity_scan=similarity_scan,
                peer_sections=peer_sections,
                similarity_exclude_document_ids=similarity_exclude_document_ids,
                survey_level=survey_level,
                report_id=report_id,
            )
            inspector = _inspector_bundle(rep)
            out["sections"][code] = {
                "executive_summary": rep.executive_summary,
                "property_description": rep.property_description,
                "condition_assessment": rep.condition_assessment,
                "defects_and_risks": rep.defects_and_risks,
                "recommendations": rep.recommendations,
                "report_text": render_report_text(
                    title=f"RICS Inspection Report — Section {code}",
                    blocks={
                        "Executive Summary": rep.executive_summary,
                        "Property Description": rep.property_description,
                        "Condition Assessment": rep.condition_assessment,
                        "Defects and Risks": rep.defects_and_risks,
                        "Recommendations": rep.recommendations,
                    },
                    section_code=code,
                    survey_level=survey_level,
                ),
                "risks": [asdict(r) for r in rep.risks],
                "compliance": [asdict(c) for c in rep.compliance],
                "evidence_items": [asdict(e) for e in rep.evidence_items],
                "tool_trace": list(rep.tool_trace),
                "inspector": inspector,
            }

            all_risks.extend(list(rep.risks))
            template = get_template(code, survey_level)
            sec_title = template.title if template else code
            section_detail_for_stitch.append(
                (
                    code,
                    sec_title,
                    rep.condition_assessment or rep.property_description or "",
                )
            )
            if rep.recommendations:
                recs.append(rep.recommendations)

    # Consolidate risks across all included sections
    consolidated = _dedupe_risks(all_risks)
    consolidated.sort(key=_risk_priority_key)

    # Build a unified executive summary using the existing adapter (keeps UK English + style).
    # Tier resolution must happen BEFORE the try/except so the fallback path
    # can pick a tier-appropriate hardcoded summary; previously `exec_lvl`
    # was set inside the try block, so the except branch defaulted to a
    # single string that contained "recommended next steps" — directive
    # advice phrasing that bypasses the L1 sanitiser and leaks into L1
    # products on any adapter failure.
    exec_lvl = _safe_level(survey_level)
    try:
        from app.llm import generation_facade as gen_llm

        ai_params = _ai_level_to_params(3, ai_percent=ai_percent)
        # Bullets for the exec summary: top risks + scope summary
        scope_bullets = [
            f"Sections covered: {', '.join(sorted(out['sections'].keys()))}",
            f"Top risks: {', '.join([f'{r.category} ({r.severity})' for r in consolidated[:5]])}" if consolidated else "Top risks: none highlighted",
        ]
        # Tier-aware exec summary length and behaviour. Without
        # ``survey_level=`` the adapter defaults to L3 word/token budgets
        # for an L1 report, producing a 600-word executive summary at the
        # top of an L1 product whose body sections cap at ~90 words —
        # an obvious tier mismatch the user could see at a glance.
        if exec_lvl <= 1:
            length_hint = (
                "Write 50–110 words. Level 1 (Condition Report) — observation only; "
                "do NOT use directive phrasing such as 'we recommend' or 'should be replaced'. "
            )
        elif exec_lvl == 2:
            length_hint = (
                "Write 100–180 words. Level 2 (HomeBuyer) — proportionate buyer-focused "
                "summary; flag practical next steps for material risks. "
            )
        else:
            length_hint = (
                "Write 140–240 words. Level 3 (Building Survey) — diagnostic summary; "
                "highlight cause/implication/options for material defects. "
            )
        exec_text = await gen_llm.generate_section(
            skeleton="Executive Summary: [overall_opinion]. [key_risks]. [next_steps].",
            bullets=scope_bullets,
            snippets=[],
            style_profile=style_profile if ai_percent > 5 else None,
            temperature=float(ai_params["temperature"]),
            creativity_hint=str(ai_params["creativity_hint"])
            + "\n\n"
            + length_hint
            + "Do not invent facts. "
            + "If something is missing or cannot be verified, omit that unsupported claim "
            + "instead of writing placeholder text.",
            document_context=[],
            hierarchy_section_snippets=None,
            paragraph_snippets=[r.risk + " — " + r.action for r in consolidated[:8]],
            style_anchor=None,
            survey_level=survey_level,
            tenant_id=tenant_id,
        )
    except Exception:  # noqa: BLE001
        # Tier-aware hardcoded fallback. The previous single-string fallback
        # contained "recommended next steps" — `_L1_ADVICE_KEYWORDS_RE` flags
        # "recommended" as forbidden L1 phrasing. Branch by tier so the L1
        # fallback never carries directive language. The L1 variant is
        # observation-only by construction; L2/L3 variants reference next
        # steps because their products legitimately include advice.
        if exec_lvl <= 1:
            exec_text = (
                "This Level 1 Condition Report summarises the inspection observations and "
                "condition ratings recorded for each element."
            )
        elif exec_lvl == 2:
            exec_text = (
                "This Level 2 Home Survey summarises the available inspection notes and "
                "supporting evidence. Key risks and proportionate next steps are highlighted "
                "below."
            )
        else:
            exec_text = (
                "This Level 3 Building Survey summarises the available inspection notes and "
                "supporting evidence. Key risks and recommended next steps are highlighted "
                "below."
            )

    # L1 sanitiser on the unified exec summary — applied OUTSIDE the
    # try/except so it runs on whichever path produced ``exec_text``. A
    # directive that leaks past the prompt-level guidance is the same
    # problem here as in the per-section path; previously this only ran
    # in the success branch, leaving the exception fallback to ship
    # advice phrasing on adapter failures.
    if exec_lvl <= 1:
        exec_text = strip_l1_advice(exec_text)

    out["consolidated_risks"] = [asdict(r) for r in consolidated]
    out["executive_summary"] = exec_text
    out["full_report_text"] = render_full_report_text(
        title="RICS Inspection Report (agentic draft)",
        executive_summary=exec_text,
        sections=section_detail_for_stitch,
        consolidated_risks=consolidated,
        recommendations=recs,
    )
    return out