app/agentic/inspector_loop.py

"""OpenAI tool-calling loop for the RICS inspector: the model chooses tools and order.

Unlike the fixed gather→draft pipeline, this module runs a multi-turn
``chat.completions`` session with ``tools=…`` until the model completes the
**server-enforced** workflow (extraction audit → section plan → optional Section C
rating summary when the active product pack includes condition ratings →
``submit_inspection_section``), or a round limit is reached.
"""

from __future__ import annotations

import asyncio
import json
import logging
import re
from typing import Any

from openai import OpenAI

from app.agentic import tools as agent_tools
from app.config import settings
from app.generator.postprocess import (
    _L1_PLACEHOLDER,
    async_enforce_verify,
    enforce_verify,
    strip_l1_advice,
    strip_l1_advice_payload,
)
from app.models.schemas import SearchResult, WritingStyleProfile
from app.services.provenance_enrichment import fetch_doc_filenames
from app.services.generation import _ai_level_to_params
from app.templates.registry import SurveyTemplatePack, get_survey_pack, get_template

from .models import EvidenceItem, RiskItem, StructuredReport

logger = logging.getLogger(__name__)

_inspector_async_client: Any = None


def _inspector_async_client() -> Any:
    """Reuse one AsyncOpenAI client across inspector tool rounds."""
    global _inspector_async_client
    if _inspector_async_client is None:
        from openai import AsyncOpenAI

        _inspector_async_client = AsyncOpenAI(api_key=settings.openai_api_key)
    return _inspector_async_client


def _strip_missing_fact_phrase(text: str) -> str:
    """Final defensive cleanup for legacy missing-data filler phrases."""
    if not isinstance(text, str) or not text:
        return text if isinstance(text, str) else ""
    cleaned = re.sub(
        r"\bInformation not provided in source document\b[.,;:!?]*",
        "",
        text,
        flags=re.IGNORECASE,
    )
    cleaned = re.sub(
        r"\bWe were unable to verify this during inspection\b[.,;:!?]*",
        "",
        cleaned,
        flags=re.IGNORECASE,
    )
    cleaned = re.sub(r"\s{2,}", " ", cleaned)
    cleaned = re.sub(r"\s+([,.;:])", r"\1", cleaned)
    return cleaned.strip()


async def _enforce_verify_submit_payload(
    payload: dict[str, str],
    *,
    bullets: list[str],
    snippets: list[str],
    peer_sections: dict[str, str] | None,
    pinned_identity: dict[str, str] | None = None,
) -> dict[str, str]:
    """Run the non-invention guard on the five free-text fields the LLM submitted.

    The agentic ``submit_inspection_section`` tool accepts five strings drafted
    by the LLM. The legacy fixed pipeline runs ``async_enforce_verify`` on every
    drafted section before persistence; the agentic path bypassed it entirely,
    which is how invented addresses, postcodes, and construction-type claims
    were reaching the user-visible report.

    We treat all of the following as "trusted source material" — anything the
    LLM uses outside of these is invention:

    - ``bullets``         : the surveyor's messy notes for THIS section
    - ``peer_sections``   : the surveyor's draft notes for sibling sections
                            (legitimate cross-section context)
    - ``snippets``        : retrieved tenant + KB chunks deduped by the loop

    The five fields are verified concurrently with ``asyncio.gather``; each call
    runs the cheap regex pass first and only falls through to the LLM grounding
    pass when an OpenAI key is configured. Cost is at most one extra mini-LLM
    call per non-empty field (~£0.0001–0.0005 each).
    """
    peer_values = list((peer_sections or {}).values())
    full_bullets = list(bullets) + [v for v in peer_values if isinstance(v, str) and v.strip()]
    field_names = (
        "executive_summary",
        "property_description",
        "condition_assessment",
        "defects_and_risks",
        "recommendations",
    )

    async def _verify_one(name: str) -> tuple[str, str]:
        text = payload.get(name) or ""
        if not text.strip():
            return name, text
        try:
            verified = await async_enforce_verify(
                text=text,
                bullets=full_bullets,
                snippets=snippets,
                pinned_identity=pinned_identity,
                openai_api_key=settings.openai_api_key or "",
                model=settings.chat_model,
            )
            return name, _strip_missing_fact_phrase(verified)
        except Exception as exc:  # noqa: BLE001
            # Grounding must never block the section — a verifier failure is
            # logged and the original LLM text is returned, matching the
            # behaviour of the legacy pipeline.
            logger.warning(
                "Non-invention guard failed for field=%s (%s); returning raw text",
                name,
                exc,
            )
            return name, _strip_missing_fact_phrase(text)

    results = await asyncio.gather(*(_verify_one(n) for n in field_names))
    return {**payload, **dict(results)}


# ---------------------------------------------------------------------------
# Comprehensive non-invention guard for ALL LLM-emitted artifacts.
# ---------------------------------------------------------------------------
# The agentic inspector emits text in *six* places, not five:
#
#   1. submit_inspection_section payload (5 user-visible body fields) — full
#      regex + LLM grounding pass via _enforce_verify_submit_payload.
#   2. submit_extraction_audit         (property_address, surveyor_name, …)
#   3. submit_section_plan             (claims[*].claim, non_claims[*])
#   4. submit_condition_rating_summary (justification, summary_markdown_table)
#   5. RiskAssessmentAgent.assess      (risk[*].risk, risk[*].action)
#   6. compliance check                (heuristic — already deterministic)
#
# Without (2)–(5) the user could see invented postcodes, surveyor names, or
# risk-action references in the metadata bundle the API returns
# (`/agentic/...` endpoints surface extraction_audit + section_plan +
# condition_rating_summary in inspector_meta). The five body fields run the
# expensive 2-layer guard because they ARE the report; auxiliary fields run
# the cheap synchronous regex pass — that catches postcodes, named entities,
# and bare numbers without adding 30+ extra LLM calls per section.


def _regex_verify_str(
    text: Any,
    *,
    bullets: list[str],
    snippets: list[str],
    pinned_identity: dict[str, str] | None = None,
) -> str:
    """Run the synchronous regex-only guard on a single string-typed field.

    Returns the input unchanged when it isn't a non-empty string. Used for
    auxiliary fields where latency / cost matters and most violations are
    short factual claims (postcodes, addresses, named persons/firms).
    """
    if not isinstance(text, str) or not text.strip():
        return text if isinstance(text, str) else ""
    return enforce_verify(
        text=text,
        bullets=bullets,
        snippets=snippets,
        pinned_identity=pinned_identity,
    )


def _verify_audit_payload(
    audit: Any,
    *,
    bullets: list[str],
    snippets: list[str],
    pinned_identity: dict[str, str] | None = None,
) -> dict[str, Any] | None:
    if not isinstance(audit, dict):
        return audit
    out = dict(audit)
    for key in (
        "property_address",
        "surveyor_name",
        "rics_number",
        "company_name",
        "inspection_date",
        "report_reference",
        "property_type",
        "construction_details",
        "services",
    ):
        if key in out:
            out[key] = _regex_verify_str(
                out.get(key),
                bullets=bullets,
                snippets=snippets,
                pinned_identity=pinned_identity,
            )
    for list_key in ("client_names", "observed_defects"):
        items = out.get(list_key)
        if isinstance(items, list):
            out[list_key] = [
                _regex_verify_str(
                    x,
                    bullets=bullets,
                    snippets=snippets,
                    pinned_identity=pinned_identity,
                )
                for x in items
                if isinstance(x, str)
            ]
    return out


def _verify_plan_payload(
    plan: Any,
    *,
    bullets: list[str],
    snippets: list[str],
    pinned_identity: dict[str, str] | None = None,
) -> dict[str, Any] | None:
    if not isinstance(plan, dict):
        return plan
    out = dict(plan)
    claims = out.get("claims")
    if isinstance(claims, list):
        verified_claims: list[dict[str, Any]] = []
        for c in claims:
            if isinstance(c, dict):
                cc = dict(c)
                if "claim" in cc:
                    cc["claim"] = _regex_verify_str(
                        cc.get("claim"),
                        bullets=bullets,
                        snippets=snippets,
                        pinned_identity=pinned_identity,
                    )
                verified_claims.append(cc)
            else:
                verified_claims.append(c)
        out["claims"] = verified_claims
    nc = out.get("non_claims")
    if isinstance(nc, list):
        out["non_claims"] = [
            _regex_verify_str(
                x,
                bullets=bullets,
                snippets=snippets,
                pinned_identity=pinned_identity,
            )
            for x in nc
            if isinstance(x, str)
        ]
    return out


def _verify_condition_payload(
    cond: Any,
    *,
    bullets: list[str],
    snippets: list[str],
    pinned_identity: dict[str, str] | None = None,
) -> dict[str, Any] | None:
    if not isinstance(cond, dict):
        return cond
    out = dict(cond)
    for k in ("justification", "summary_markdown_table"):
        if k in out:
            out[k] = _regex_verify_str(
                out.get(k),
                bullets=bullets,
                snippets=snippets,
                pinned_identity=pinned_identity,
            )
    return out


def _verify_risks(
    risks: list[RiskItem],
    *,
    bullets: list[str],
    snippets: list[str],
    pinned_identity: dict[str, str] | None = None,
) -> list[RiskItem]:
    """Strip invented postcodes / addresses / named firms from LLM-drafted risks.

    `RiskItem` is frozen, so we rebuild each item; `category`, `severity`, and
    `likelihood` are constrained to known enum values upstream and never need
    verification, so we only sweep `risk` and `action`.
    """
    out: list[RiskItem] = []
    for r in risks:
        try:
            out.append(
                RiskItem(
                    category=r.category,
                    risk=_regex_verify_str(
                        r.risk,
                        bullets=bullets,
                        snippets=snippets,
                        pinned_identity=pinned_identity,
                    ),
                    severity=r.severity,
                    likelihood=r.likelihood,
                    action=_regex_verify_str(
                        r.action,
                        bullets=bullets,
                        snippets=snippets,
                        pinned_identity=pinned_identity,
                    ),
                    evidence=r.evidence,
                )
            )
        except Exception as exc:  # noqa: BLE001
            logger.warning("Risk verification failed (%s); keeping raw item", exc)
            out.append(r)
    return out


def _openai_inspector_tool_definitions() -> list[dict[str, Any]]:
    """Narrow tool schemas: tenant/document IDs are injected server-side."""
    return [
        {
            "type": "function",
            "function": {
                "name": "retrieve_survey_rag",
                "description": (
                    "Search the tenant's indexed RAG corpus (uploaded survey PDF/DOCX and reference "
                    "library), prioritising the report's primary document. Call this when you need "
                    "verbatim or technical evidence from the uploaded survey before writing findings."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "Semantic query distilled from the inspection notes (concise).",
                        },
                        "k": {
                            "type": "integer",
                            "description": "Max chunks to retrieve before reranking (default 14).",
                            "minimum": 1,
                            "maximum": 30,
                        },
                        "rerank_top_n": {
                            "type": "integer",
                            "description": "How many chunks to return after reranking (default 7).",
                            "minimum": 1,
                            "maximum": 15,
                        },
                    },
                    "required": ["query"],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "retrieve_rics_kb",
                "description": (
                    "Search the local RICS standards / exemplar knowledge base for professional "
                    "wording, definitions, and compliance framing. Use when notes are thin or you "
                    "need authoritative context (still ground factual claims in survey RAG)."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {"type": "string"},
                        "k": {"type": "integer", "description": "Max KB chunks to fetch (default 10).", "minimum": 1, "maximum": 20},
                        "rerank_top_n": {"type": "integer", "description": "Chunks to keep after rerank (default 5).", "minimum": 1, "maximum": 10},
                        "hierarchy_level": {
                            "type": "string",
                            "enum": ["document", "section", "paragraph"],
                            "description": "Optional granularity filter when the index supports hierarchy.",
                        },
                    },
                    "required": ["query"],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "scan_note_duplicates",
                "description": (
                    "Check the tenant's indexed library for semantically similar passages and "
                    "detect near-duplicate wording across other sections' drafts. Use when notes "
                    "may overlap prior reports or other sections."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "text": {"type": "string", "description": "Notes block to compare (e.g. joined bullets)."},
                        "section_code": {"type": "string"},
                        "peer_sections": {
                            "type": "object",
                            "additionalProperties": {"type": "string"},
                            "description": "Map section_code → draft text for cross-section overlap.",
                        },
                        "exclude_document_ids": {
                            "type": "array",
                            "items": {"type": "string"},
                            "description": "Upload UUIDs to omit from library similarity (e.g. primary survey).",
                        },
                    },
                    "required": ["text"],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "submit_extraction_audit",
                "description": (
                    "MANDATORY (server-enforced before final submit): structured extraction from the "
                    "inspection notes and survey RAG snippets — what is evidenced vs unknown. "
                    "Call once per section after you have run retrieve_survey_rag / retrieve_rics_kb "
                    "enough to understand the fact basis."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "property_address": {"type": "string"},
                        "client_names": {"type": "array", "items": {"type": "string"}},
                        "surveyor_name": {"type": "string"},
                        "rics_number": {"type": "string"},
                        "company_name": {"type": "string"},
                        "inspection_date": {"type": "string"},
                        "report_reference": {"type": "string"},
                        "property_type": {"type": "string"},
                        "construction_details": {"type": "string"},
                        "services": {"type": "string"},
                        "observed_defects": {"type": "array", "items": {"type": "string"}},
                        "limitations_to_inspection": {"type": "array", "items": {"type": "string"}},
                        "assumptions": {"type": "array", "items": {"type": "string"}},
                    },
                    "required": [
                        "property_address",
                        "client_names",
                        "surveyor_name",
                        "rics_number",
                        "company_name",
                        "inspection_date",
                        "report_reference",
                        "property_type",
                        "construction_details",
                        "services",
                        "observed_defects",
                        "limitations_to_inspection",
                        "assumptions",
                    ],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "submit_section_plan",
                "description": (
                    "MANDATORY (server-enforced before final submit): a short plan mapping claims to "
                    "evidence chunk_ids / doc_ids you intend to rely on. Ratings (1/2/3/NI) must appear "
                    "only where the active RICS product tier uses condition ratings for that element group."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "claims": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "claim": {"type": "string"},
                                    "evidence_refs": {
                                        "type": "array",
                                        "items": {"type": "string"},
                                        "description": "chunk_id values and/or doc_id:chunk_id pairs from tool hits.",
                                    },
                                    "risk": {
                                        "type": "string",
                                        "enum": ["low", "medium", "high"],
                                    },
                                },
                                "required": ["claim", "evidence_refs", "risk"],
                            },
                        },
                        "non_claims": {
                            "type": "array",
                            "items": {"type": "string"},
                            "description": "Boilerplate you will keep generic (no site-specific facts).",
                        },
                    },
                    "required": ["claims", "non_claims"],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "submit_condition_rating_summary",
                "description": (
                    "When the active survey product includes condition ratings anywhere in its template "
                    "pack, Section C must summarise ratings in a markdown table before the final submit. "
                    "If ratings truly do not apply for this property type, set ratings_not_applicable=true "
                    "with a one-line justification instead of inventing counts."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "ratings_not_applicable": {"type": "boolean"},
                        "justification": {"type": "string"},
                        "summary_markdown_table": {
                            "type": "string",
                            "description": "Markdown table with columns Category | Count NI | Count 3 | Count 2 | Count 1",
                        },
                    },
                    "required": ["ratings_not_applicable"],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "submit_inspection_section",
                "description": (
                    "Submit the final drafted content for this RICS section. Server will reject this "
                    "until submit_extraction_audit and submit_section_plan have been accepted. "
                    "When the product pack includes condition ratings, Section C also requires "
                    "submit_condition_rating_summary first. "
                    "Ground factual statements in retrieve_survey_rag / retrieve_rics_kb results. "
                    "If information is missing, omit the claim rather than writing placeholder "
                    "sentences. Never insert missing-data text mid-sentence. "
                    "UK English, MRICS tone. Call alone (no other tools in the same assistant message). "
                    "PRODUCE PROPERTY-SPECIFIC PROSE AT THE TIER-APPROPRIATE LENGTH — NOT SUMMARIES, "
                    "NOR PADDING. Each free-text field below is the user-visible report; aim at the "
                    "per-tier word_targets in the SYSTEM PROMPT (L1 condensed observation, L2 "
                    "proportionate buyer advice, L3 diagnostic depth). Do NOT collapse an L3 field to "
                    "one or two sentences; do NOT inflate an L1 field beyond its observation-only "
                    "range. Cite specific defects, locations, materials, and observed evidence — "
                    "never generic boilerplate."
                ),
                "parameters": {
                    "type": "object",
                    # Schema descriptions deliberately defer length / depth to
                    # the per-tier SYSTEM PROMPT (word_targets + depth_directive
                    # in :func:`_inspector_system_prompt`) instead of baking in
                    # L3-leaning ranges. Otherwise the model sees "target
                    # 200–500 words" for condition_assessment and tries to hit
                    # that even when the active tier is L1 (40–90 words), or
                    # tries a Level-3-style cause/implications/options layering
                    # in an L1 product (which is forbidden).
                    "properties": {
                        "executive_summary": {
                            "type": "string",
                            "description": (
                                "Headline summary for THIS section. Length per tier — see SYSTEM "
                                "PROMPT word_targets. State scope of inspection, principal "
                                "observations, and overall outcome. Avoid generic openings like "
                                "'This section covers…'."
                            ),
                        },
                        "property_description": {
                            "type": "string",
                            "description": (
                                "Specific factual description of the section subject. Length per "
                                "tier — see SYSTEM PROMPT word_targets. Cover construction type, "
                                "materials, accommodation, age, services, and dimensions WHEN "
                                "supported by bullets/snippets. Use the approved missing-info "
                                "phrase only for facts truly unavailable."
                            ),
                        },
                        "condition_assessment": {
                            "type": "string",
                            "description": (
                                "Condition narrative. Length and depth per tier — see SYSTEM "
                                "PROMPT word_targets and depth_directive (L1 observation-only; "
                                "L2 proportionate; L3 cause → implications → options). Cite each "
                                "defect with location and severity at the depth appropriate to "
                                "the active tier; do not over-extend an L1 paragraph or under-fill "
                                "an L3 one."
                            ),
                        },
                        "defects_and_risks": {
                            "type": "string",
                            "description": (
                                "Defects mapped to category (structural / moisture / electrical / "
                                "etc.), with location, cause (where supported), and consequence. "
                                "Length per tier — see SYSTEM PROMPT word_targets. List each "
                                "evidenced defect; do not omit any from the bullets."
                            ),
                        },
                        "recommendations": {
                            "type": "string",
                            "description": (
                                "Actionable recommendations with urgency, tied to an observed "
                                "defect or evidence reference. Length per tier — see SYSTEM "
                                "PROMPT word_targets. For Level 1 (Condition Report) the system "
                                "prompt forbids advice — in that case write only that no "
                                "recommendations are issued at this tier, and stop."
                            ),
                        },
                    },
                    "required": [
                        "executive_summary",
                        "property_description",
                        "condition_assessment",
                        "defects_and_risks",
                        "recommendations",
                    ],
                },
            },
        },
    ]


def _compact_hits_for_llm(hits: list[SearchResult], *, max_items: int = 8, max_chars: int = 520) -> list[dict[str, Any]]:
    out: list[dict[str, Any]] = []
    for r in hits[:max_items]:
        t = (r.text or "").strip().replace("\n", " ")
        if len(t) > max_chars:
            t = t[: max_chars - 1] + "…"
        out.append(
            {
                "chunk_id": r.chunk_id,
                "doc_id": r.doc_id,
                "score": round(float(r.score), 5),
                "kb": bool(getattr(r, "kb", False)),
                "text": t,
            }
        )
    return out


def _pack_has_condition_ratings(pack: SurveyTemplatePack) -> bool:
    return any(bool(t.has_condition_rating) for t in pack._by_code.values())


def _section_c_requires_rating_summary(*, section_code: str, pack: SurveyTemplatePack) -> bool:
    if section_code != "C":
        return False
    return _pack_has_condition_ratings(pack)


def _norm_str_list(val: Any, *, min_items: int, max_items: int, max_item_len: int) -> tuple[str, list[str] | None]:
    if not isinstance(val, list):
        return ("must be a JSON array of strings", None)
    out: list[str] = []
    for x in val[:max_items]:
        s = str(x).strip()
        if not s:
            continue
        if len(s) > max_item_len:
            s = s[: max_item_len - 1] + "…"
        out.append(s)
    if len(out) < min_items:
        return (f"need at least {min_items} non-empty string item(s)", None)
    return ("", out)


def _validate_extraction_audit(args: dict[str, Any]) -> tuple[str, dict[str, Any] | None]:
    # Mandatory extraction schema for reconstruction. Use the two approved phrases when unknown.
    required_str_fields = [
        "property_address",
        "surveyor_name",
        "rics_number",
        "company_name",
        "inspection_date",
        "report_reference",
        "property_type",
        "construction_details",
        "services",
    ]
    out: dict[str, Any] = {}
    errs: list[str] = []

    for k in required_str_fields:
        v = str(args.get(k, "")).strip()
        if not v:
            errs.append(f"{k} is required")
        elif len(v) > 1200:
            out[k] = v[:1199] + "…"
        else:
            out[k] = v

    cn = args.get("client_names")
    if not isinstance(cn, list):
        errs.append("client_names must be a JSON array of strings")
        client_names = None
    else:
        client_names = [str(x).strip() for x in cn if str(x).strip()][:12]
        if not client_names:
            errs.append("client_names must contain at least one name or an approved missing-info phrase")
    if client_names is not None:
        out["client_names"] = client_names

    od_msg, observed_defects = _norm_str_list(args.get("observed_defects"), min_items=0, max_items=40, max_item_len=420)
    if od_msg:
        errs.append(f"observed_defects: {od_msg}")
    else:
        out["observed_defects"] = observed_defects

    lim_msg, limitations = _norm_str_list(args.get("limitations_to_inspection"), min_items=0, max_items=24, max_item_len=420)
    if lim_msg:
        errs.append(f"limitations_to_inspection: {lim_msg}")
    else:
        out["limitations_to_inspection"] = limitations

    asm_msg, assumptions = _norm_str_list(args.get("assumptions"), min_items=0, max_items=16, max_item_len=420)
    if asm_msg:
        errs.append(f"assumptions: {asm_msg}")
    else:
        out["assumptions"] = assumptions

    if errs:
        return ("; ".join(errs), None)
    return ("", out)


def _validate_section_plan(args: dict[str, Any]) -> tuple[str, dict[str, Any] | None]:
    claims_raw = args.get("claims")
    if not isinstance(claims_raw, list) or not claims_raw:
        return ("claims must be a non-empty JSON array", None)
    claims_out: list[dict[str, Any]] = []
    for c in claims_raw[:30]:
        if not isinstance(c, dict):
            return ("each claim must be an object", None)
        claim = str(c.get("claim", "")).strip()
        if len(claim) < 12:
            return ("each claim.claim must be a substantive string", None)
        refs = c.get("evidence_refs")
        if not isinstance(refs, list) or not refs:
            return ("each claim needs a non-empty evidence_refs array", None)
        refs_s = [str(x).strip() for x in refs if str(x).strip()][:24]
        if not refs_s:
            return ("evidence_refs must contain non-empty strings", None)
        risk = str(c.get("risk", "")).strip().lower()
        if risk not in ("low", "medium", "high"):
            return ("claim.risk must be one of: low | medium | high", None)
        claims_out.append({"claim": claim[:900], "evidence_refs": refs_s, "risk": risk})

    nc_msg, non_claims = _norm_str_list(args.get("non_claims"), min_items=0, max_items=24, max_item_len=300)
    if nc_msg:
        return (nc_msg, None)
    assert non_claims is not None
    return ("", {"claims": claims_out, "non_claims": non_claims})


def _validate_condition_rating_summary(args: dict[str, Any]) -> tuple[str, dict[str, Any] | None]:
    na = args.get("ratings_not_applicable")
    if not isinstance(na, bool):
        return ("ratings_not_applicable must be a boolean", None)
    justification = str(args.get("justification", "")).strip()
    table = str(args.get("summary_markdown_table", "")).strip()
    if na:
        if len(justification) < 12:
            return ("when ratings_not_applicable=true, justification must explain why (substantive).", None)
        return ("", {"ratings_not_applicable": True, "justification": justification[:800], "summary_markdown_table": ""})
    lowered = table.lower()
    if "|" not in table or "\n" not in table:
        return ("summary_markdown_table must be a markdown table (header + separator + rows).", None)
    for token in ("ni", "3", "2", "1"):
        if token not in lowered:
            return ("table must include NI and numeric rating columns/labels for 3, 2, and 1.", None)
    return ("", {"ratings_not_applicable": False, "justification": "", "summary_markdown_table": table[:6000]})


async def _dispatch_tool(
    *,
    db,
    tenant_id: str,
    primary_document_id: str,
    reference_document_ids: list[str] | None,
    default_hierarchy_level: str | None,
    peer_sections_default: dict[str, str] | None,
    section_code: str,
    pack: SurveyTemplatePack,
    gate: dict[str, Any],
    name: str,
    args: dict[str, Any],
) -> tuple[str, list[SearchResult]]:
    """Run one tool; return JSON string for the assistant plus any new search hits."""
    extra_hits: list[SearchResult] = []
    try:
        if name == "retrieve_survey_rag":
            q = str(args.get("query", "")).strip()
            if not q:
                return json.dumps({"error": "empty query"}), []
            k = int(args.get("k", 14))
            rn = int(args.get("rerank_top_n", 7))
            k = max(4, min(48, k))
            rn = max(2, min(16, rn))
            hits = await agent_tools.retrieve_tenant_evidence_async(
                query=q,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                secondary_document_ids=reference_document_ids,
                k=k,
                rerank_top_n=rn,
            )
            extra_hits.extend(hits)
            return json.dumps({"hits": _compact_hits_for_llm(hits)}), hits

        if name == "retrieve_rics_kb":
            q = str(args.get("query", "")).strip()
            if not q:
                return json.dumps({"hits": [], "message": "KB disabled or empty query"}), []
            k = int(args.get("k", 10))
            rn = int(args.get("rerank_top_n", 5))
            hl = args.get("hierarchy_level")
            if isinstance(hl, str) and hl not in ("document", "section", "paragraph"):
                hl = default_hierarchy_level
            elif hl is None:
                hl = default_hierarchy_level if default_hierarchy_level in ("document", "section", "paragraph") else None
            hits = await agent_tools.retrieve_kb_guidance_async(
                query=q, k=k, hierarchy_level=hl, rerank_top_n=rn
            )
            extra_hits.extend(hits)
            return json.dumps({"hits": _compact_hits_for_llm(hits), "kb_enabled": settings.knowledge_base_enabled}), hits

        if name == "scan_note_duplicates":
            text = str(args.get("text", "")).strip()
            if not text:
                return json.dumps({"library_matches": [], "draft_overlaps": [], "message": "no text"}), []
            peers = args.get("peer_sections")
            if not isinstance(peers, dict):
                peers = dict(peer_sections_default or {})
            sec = args.get("section_code")
            sec_s = str(sec) if sec is not None else None
            excl = args.get("exclude_document_ids")
            excl_l = [str(x) for x in excl] if isinstance(excl, list) else []
            sim = await agent_tools.find_similar_library_and_peers(
                db,
                tenant_id,
                text=text,
                section_code=sec_s,
                peer_sections=peers,
                exclude_document_ids=excl_l,
            )
            payload = {
                "library_matches": [m.model_dump() for m in sim.library_matches[:12]],
                "draft_overlaps": [m.model_dump() for m in sim.draft_overlaps[:12]],
                "message": (sim.message or "")[:500],
            }
            return json.dumps(payload), []

        if name == "submit_extraction_audit":
            err, payload = _validate_extraction_audit(args)
            if err or payload is None:
                return json.dumps({"ok": False, "error": err or "invalid payload"}), []
            gate["audit_payload"] = payload
            return json.dumps({"ok": True, "accepted": "extraction_audit"}), []

        if name == "submit_section_plan":
            err, payload = _validate_section_plan(args)
            if err or payload is None:
                return json.dumps({"ok": False, "error": err or "invalid payload"}), []
            if gate.get("audit_payload") is None:
                return json.dumps(
                    {
                        "ok": False,
                        "error": "submit_extraction_audit must be accepted before submit_section_plan.",
                    }
                ), []
            gate["plan_payload"] = payload
            return json.dumps({"ok": True, "accepted": "section_plan"}), []

        if name == "submit_condition_rating_summary":
            err, payload = _validate_condition_rating_summary(args)
            if err or payload is None:
                return json.dumps({"ok": False, "error": err or "invalid payload"}), []
            if not _section_c_requires_rating_summary(section_code=section_code, pack=pack):
                return json.dumps(
                    {
                        "ok": False,
                        "error": "submit_condition_rating_summary is only used for Section C when the active product pack includes condition ratings.",
                    }
                ), []
            # Enforce RICS-like grouping: the table must mention every rated element group prefix
            # present in this product pack (e.g. E/F/G for Building Survey).
            required_prefixes = sorted(
                {t.code[0] for t in pack._by_code.values() if t.has_condition_rating and t.code and t.code[0].isalpha()}
            )
            table = str(payload.get("summary_markdown_table") or "")
            if required_prefixes and table:
                missing_prefixes = []
                for pfx in required_prefixes:
                    # row like "| E1" or "| E "
                    if f"| {pfx}" not in table and f"|{pfx}" not in table:
                        missing_prefixes.append(pfx)
                if missing_prefixes:
                    return json.dumps(
                        {
                            "ok": False,
                            "error": (
                                "Condition rating summary table must include rows for these element groups "
                                f"(from the active template pack): {', '.join(missing_prefixes)}."
                            ),
                        }
                    ), []
            gate["condition_payload"] = payload
            return json.dumps({"ok": True, "accepted": "condition_rating_summary"}), []

        if name == "submit_inspection_section":
            if gate.get("audit_payload") is None:
                return json.dumps(
                    {"ok": False, "error": "Call and pass submit_extraction_audit before submit_inspection_section."}
                ), []
            if gate.get("plan_payload") is None:
                return json.dumps(
                    {"ok": False, "error": "Call and pass submit_section_plan before submit_inspection_section."}
                ), []
            if _section_c_requires_rating_summary(section_code=section_code, pack=pack) and gate.get("condition_payload") is None:
                return json.dumps(
                    {
                        "ok": False,
                        "error": "Section C in this product tier requires submit_condition_rating_summary before submit_inspection_section.",
                    }
                ), []
            gate["submit_payload"] = {
                "executive_summary": str(args.get("executive_summary", "")).strip(),
                "property_description": str(args.get("property_description", "")).strip(),
                "condition_assessment": str(args.get("condition_assessment", "")).strip(),
                "defects_and_risks": str(args.get("defects_and_risks", "")).strip(),
                "recommendations": str(args.get("recommendations", "")).strip(),
            }
            return json.dumps({"ok": True, "accepted": "submit_inspection_section"}), []

    except Exception as exc:  # noqa: BLE001
        logger.exception("inspector tool %s failed", name)
        return json.dumps({"error": str(exc)}), []

    return json.dumps({"error": f"unknown tool {name}"}), []


async def _evidence_items_async(db, tenant_id: str, uniq: list[SearchResult]) -> list[EvidenceItem]:
    tenant_doc_ids = {r.doc_id for r in uniq if r.tenant_id == tenant_id and r.doc_id}
    filenames: dict[str, str | None] = {}
    if db is not None and tenant_doc_ids:
        filenames = await fetch_doc_filenames(db, tenant_id, tenant_doc_ids)
    items: list[EvidenceItem] = []
    for r in uniq:
        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"
        items.append(
            EvidenceItem(
                doc_id=r.doc_id,
                chunk_id=r.chunk_id,
                score=float(r.score),
                text=r.text,
                source=str(src) if src else None,
                section_hint=getattr(r, "section_title", None),
                kb=is_kb,
            )
        )
    return items


def _scale_inspector_word_targets(
    *,
    lvl: int,
    bullets_word_count: int,
) -> str:
    """Render the per-field word_targets clause, scaling upper bounds when bullets are dense.

    Without scaling, an L3 ``condition_assessment ~60–120 words`` ceiling
    forces the LLM to compress 500 words of dense field notes into a 120-word
    paragraph and silently drops detail. The scaled upper bound widens the
    ceiling proportional to input density so every observation can land in
    the output. We do NOT change the lower bound — the floor stays the same.
    """
    if lvl >= 3:
        spec = [
            ("executive_summary", 30, 60),
            ("property_description", 35, 70),
            ("condition_assessment", 60, 120),
            ("defects_and_risks", 35, 80),
            ("recommendations", 20, 45),
        ]
    elif lvl == 2:
        spec = [
            ("executive_summary", 20, 45),
            ("property_description", 25, 50),
            ("condition_assessment", 45, 90),
            ("defects_and_risks", 25, 55),
            ("recommendations", 15, 35),
        ]
    else:
        spec = [
            ("executive_summary", 45, 85),
            ("property_description", 55, 110),
            ("condition_assessment", 110, 220),
            ("defects_and_risks", 55, 120),
            ("recommendations", 30, 70),
        ]

    bw = max(0, int(bullets_word_count or 0))
    if bw <= 0:
        parts = [f"{name} ~{lo}–{hi} words" for name, lo, hi in spec]
        return ", ".join(parts) + ". "

    # Distribute the dense-notes word budget across fields in proportion to
    # their default ceilings, then take the max of the static ceiling and
    # the scaled budget. Absolute cap mirrors the prompts.py module so the
    # two pipelines stay in sync at ~2400 words/section.
    base_total = sum(hi for _, _, hi in spec) or 1
    budget = max(0, int(round(bw * 0.85)))
    abs_cap_per_field = 1200
    parts: list[str] = []
    for name, lo, hi in spec:
        share = int(round(budget * (hi / base_total)))
        new_hi = min(max(hi, hi + share), abs_cap_per_field)
        if new_hi <= hi:
            parts.append(f"{name} ~{lo}–{hi} words")
        else:
            parts.append(
                f"{name} ~{lo}–{new_hi} words (notes are dense — exceed the default ceiling to preserve every observation)"
            )
    return ", ".join(parts) + ". "


def _inspector_system_prompt(
    *,
    tenant_id: str,
    primary_document_id: str,
    survey_level: int | None,
    pack: SurveyTemplatePack,
    section_code: str,
    section_title: str,
    style_profile: WritingStyleProfile | None,
    identity_block: str | None = None,
    bullets_word_count: int = 0,
) -> str:
    tone = style_profile.tone if style_profile and style_profile.tone else "professional, precise UK building surveyor"
    order_preview = ", ".join(list(pack.section_order)[:34])
    if len(pack.section_order) > 34:
        order_preview += ", …"
    ratings_pack = _pack_has_condition_ratings(pack)
    c_extra = ""
    if section_code == "C" and ratings_pack:
        c_extra = (
            " For Section C in this product tier, you must also call submit_condition_rating_summary "
            "(markdown rating table, or ratings_not_applicable with justification) before the final submit."
        )

    # ── Per-tier depth directive ───────────────────────────────────────────
    # Real RICS L3 element narratives describe each defect as
    # cause → implications → options/next steps, in continuous diagnostic
    # prose. Telling the model the *upper* end of the word range (and that
    # L3 specifically must layer cause/implications/options) is what moves
    # gpt-4o-mini from "150-word HomeBuyer-style paragraph" to actual
    # diagnostic depth. Without an explicit L3 directive the model defaults
    # to its trained-average paragraph length, which is roughly L2.
    try:
        lvl = int(survey_level if survey_level is not None else pack.level)
    except Exception:  # noqa: BLE001
        lvl = pack.level
    if lvl >= 3:
        depth_directive = (
            "DEPTH FOR LEVEL 3 (BUILDING SURVEY — DIAGNOSTIC MODE): aim at the UPPER end of every "
            "word range below. For each material defect, weave (within the relevant field): (a) what "
            "was observed, (b) the likely cause/mechanism (only if supported by evidence), "
            "(c) implications/risks if unaddressed, and (d) options/next steps. Do NOT collapse "
            "into HomeBuyer-style brevity. Each defect must surface in defects_and_risks AND "
            "carry through to recommendations with a tied action. "
        )
    elif lvl == 2:
        depth_directive = (
            "DEPTH FOR LEVEL 2 (HomeBuyer): proportionate buyer-focused advice. Cover practical next "
            "steps for material defects (e.g. obtain quotations, further checks). Avoid deep "
            "diagnostic speculation unless the bullets/snippets explicitly support it. "
        )
    else:
        depth_directive = (
            "DEPTH FOR LEVEL 1 (Condition Report — observation mode): record condition concisely. "
            "Do NOT advise on repairs, recommend works, or use directive phrasing such as "
            "'we recommend' or 'should be replaced'. Keep paragraphs factual and observation-only. "
        )
    word_targets = _scale_inspector_word_targets(lvl=lvl, bullets_word_count=bullets_word_count)

    identity_block_clean = (identity_block or "").strip()
    identity_clause = (
        "PROPERTY IDENTITY (NON-NEGOTIABLE, must be reused verbatim — do NOT introduce any "
        "different address, postcode, property type, surveyor name, or company name):\n"
        f"{identity_block_clean}\n\n"
        if identity_block_clean
        else ""
    )

    return (
        "You are a Chartered Building Surveyor (MRICS) producing a RICS-style inspection report section. "
        "You decide which retrieval tools to call and how many times—there is no fixed script for RAG/KB. "
        "Ground factual statements in retrieve_survey_rag and/or retrieve_rics_kb results; "
        "use scan_note_duplicates when messy notes might duplicate other sections or prior library text. "
        "Never invent site-specific facts. If a fact is missing from bullets, seed evidence, retrieve_* "
        "results, and PROPERTY IDENTITY pins, omit that claim instead of using placeholder text. "
        "Never inline missing-data wording in the middle of a sentence. "
        + identity_clause +
        # ── Anti-summarisation + per-tier depth directive ─────────────────
        # The depth directive replaces the previous static word-target list,
        # which gave the same range for every level. With per-tier targets
        # gpt-4o-mini stops collapsing L3 sections into HomeBuyer-length
        # paragraphs.
        "PRODUCE COMPREHENSIVE PROPERTY-SPECIFIC ANALYSIS, NOT SUMMARIES. The five free-text fields "
        "in submit_inspection_section ARE the user-visible report. Each one must be substantive "
        "RICS-grade prose grounded in bullets and retrieved snippets — for THIS tier: "
        + word_targets
        + "Do NOT pad with generic boilerplate. Do NOT shorten to one or two sentences. "
        "When a topic is sparsely evidenced, retrieve more with retrieve_survey_rag or retrieve_rics_kb "
        "rather than truncating. Use the approved missing-info phrases ONLY for facts you genuinely "
        "cannot verify in the bullets/snippets — never as a substitute for analysis. Surface all "
        "evidenced defects from the bullets; do not omit any. "
        + depth_directive +
        f"Writing tone guidance: {tone}. "
        f"Server context (inject into tool calls automatically where applicable): tenant_id={tenant_id!r}, "
        f"primary_document_id={primary_document_id!r}. "
        f"RICS product tier: Level {pack.level} — {pack.product_label}. "
        f"Client survey_level field: {survey_level!r} (None is treated as Level 3 for template pack resolution). "
        f"Mandatory section order for this tier (codes): {order_preview}. "
        f"Current section: {section_code} — {section_title}. "
        "SERVER-ENFORCED WORKFLOW (tools): "
        "(1) retrieve evidence as needed → "
        "(2) submit_extraction_audit → "
        "(3) submit_section_plan → "
        f"(4){' submit_condition_rating_summary →' if section_code == 'C' and ratings_pack else ''} "
        "(5) submit_inspection_section (exactly once). "
        "Do not call submit_inspection_section in the same assistant message as any other tool. "
        "UK English. "
        # ── RAW NOTES COVERAGE CONTRACT ───────────────────────────────────
        # The user wants EVERY observation in the messy notes to land in
        # the rendered report; the agentic pipeline previously summarised
        # 500-word note dumps into ~120 words and silently dropped
        # observations. This clause is paired with the per-section
        # ``coverage_report`` post-check that triggers up to 3 regenerates
        # when measured coverage < 70%.
        "RAW NOTES COVERAGE CONTRACT: every distinct observation in the inspector's bullets "
        "MUST surface in the relevant submit_inspection_section field. Do NOT compress two "
        "bullets into one generic clause. Do NOT drop a bullet because it sounds repetitive "
        "— in field notes, repetition usually means the same defect was observed in more than "
        "one location, and every location must be preserved. When the notes are dense, exceed "
        "the default word ceiling rather than truncating; the per-field upper bounds above "
        "are themselves already scaled to the notes' word count."
        f"{c_extra}"
    )


async def run_inspector_tool_loop(
    *,
    db,
    tenant_id: str,
    primary_document_id: str,
    section_code: str,
    bullets: list[str],
    style_profile: WritingStyleProfile | None,
    ai_percent: int,
    retrieval_level: str,
    reference_document_ids: list[str] | None,
    peer_sections: dict[str, str] | None,
    survey_level: int | None = None,
) -> tuple[StructuredReport, list[dict[str, Any]]]:
    """Run the autonomous inspector loop; returns structured report + tool trace."""
    trace: list[dict[str, Any]] = []
    from app.agentic.speculative_executor import (
        PatternRegistry,
        SpeculativeToolDispatcher,
        speculative_execution_active,
    )

    _spec_registry = PatternRegistry()
    _spec_context: dict[str, Any] = {
        "section_code": section_code,
        "outline": "",
    }
    pack = get_survey_pack(survey_level)
    template = get_template(section_code, survey_level)
    section_title = template.title if template else section_code
    skeleton = (template.skeleton if template else "")[:2000]
    _spec_context["outline"] = skeleton

    # ── Pre-seeded grounding (key fix for "LLM invents because it had no evidence")
    # Without an upfront retrieval seed, the inspector loop relied entirely on
    # the LLM to discover useful queries via retrieve_survey_rag. When the
    # bullets are sparse (the user's exact complaint scenario — messy notes
    # uploaded for an L3 section), the LLM's first query is often vague
    # ("section E2 condition"), the result set is bland, the LLM gives up
    # retrieving and falls back to its training-data prior. That's where
    # "10 Kingsley Avenue", "robust steel frame", and made-up surveyor names
    # come from.
    #
    # We now do a deterministic up-front retrieval against the primary
    # uploaded survey (and any user-tagged reference docs) and inject the
    # top hits straight into the user message as SEED EVIDENCE, plus
    # extracted IDENTITY FACTS and STYLE REFERENCE PARAGRAPHS from the
    # tenant's profile. The LLM still has full freedom to call additional
    # retrieve_* tools, but starts with concrete grounded material — which
    # both reduces invention and gives the verifier real text to compare
    # the draft against.
    #
    # Local imports keep the cycle clean: the agent tools layer already
    # imports inspector_loop indirectly via agents.py.
    from app.agentic import tools as agent_tools_local
    from app.services.generation import _extract_property_identity, _identity_facts_block

    seed_hits: list[SearchResult] = []
    seed_query = " ".join(
        [section_code, section_title]
        + [b for b in bullets if isinstance(b, str) and b.strip()][:12]
    ).strip()
    # Tier-aware seed retrieval window. Previously L1 and L2 received the
    # same window (k=16/rerank=6), which under-served L2 (HomeBuyer needs
    # enough evidence to give *proportionate* advice — i.e. not just
    # observation, but options-light) and over-served L1 (observation-only,
    # narrower scope). The new split:
    #   L1 — k=12 / rerank=5  (observation-only, condensed RAG window)
    #   L2 — k=18 / rerank=8  (HomeBuyer; more evidence than L1, less than L3)
    #   L3 — k=24 / rerank=10 (Building Survey; full diagnostic context)
    effective_level = survey_level if survey_level is not None else pack.level
    try:
        _seed_lvl = int(effective_level)
    except Exception:  # noqa: BLE001
        _seed_lvl = pack.level
    if _seed_lvl >= 3:
        seed_k, seed_rerank = 24, 10
    elif _seed_lvl == 2:
        seed_k, seed_rerank = 18, 8
    else:
        seed_k, seed_rerank = 12, 5

    if seed_query:
        try:
            seed_hits = await agent_tools_local.retrieve_tenant_evidence_async(
                query=seed_query,
                tenant_id=tenant_id,
                primary_document_id=primary_document_id,
                secondary_document_ids=list(reference_document_ids or []),
                k=seed_k,
                rerank_top_n=seed_rerank,
            )
        except Exception as exc:  # noqa: BLE001
            logger.warning("Inspector seed retrieval failed (%s); proceeding without pre-seed", exc)
            seed_hits = []
    trace.append({"event": "seed_retrieval", "hits": len(seed_hits), "query_preview": seed_query[:120]})

    identity = _extract_property_identity(list(bullets))
    if seed_hits:
        # Identity often appears in the primary doc but not in messy bullets
        # — e.g. the surveyor uploaded the property's own description PDF
        # but only typed a few free-form notes for THIS section. Letting the
        # extractor see the seed snippets too means the address it pins is
        # the address from the actual uploaded survey, not a parsed-out
        # bullet fragment. Trim each snippet to keep extraction fast.
        ext_lines = list(bullets) + [r.text[:1000] for r in seed_hits[:6] if getattr(r, "text", None)]
        identity = _extract_property_identity(ext_lines)
    identity_block = _identity_facts_block(identity)

    seed_evidence_block = ""
    if seed_hits:
        # Mirror the seed retrieval tier ladder: don't ship more snippets to
        # the LLM than were retrieved, and don't bloat the L1 prompt with
        # context the L1 mode won't use.
        if _seed_lvl >= 3:
            max_seed = 10
        elif _seed_lvl == 2:
            max_seed = 8
        else:
            max_seed = 5
        seed_lines: list[str] = []
        for i, r in enumerate(seed_hits[:max_seed], 1):
            t = (r.text or "").strip().replace("\n", " ")
            if len(t) > 600:
                t = t[:599] + "…"
            tag_kb = "(KB)" if bool(getattr(r, "kb", False)) else "(uploaded)"
            seed_lines.append(f"[#{i}] {tag_kb} {t}")
        seed_evidence_block = (
            "\n\nSEED EVIDENCE FROM YOUR UPLOADED RAG CORPUS (primary grounding — facts in here "
            "are TRUSTED; quote or paraphrase rather than invent):\n"
            + "\n\n".join(seed_lines)
        )

    style_examples_block = ""
    if style_profile is not None:
        examples = [
            p for p in (getattr(style_profile, "example_paragraphs", []) or [])
            if isinstance(p, str) and p.strip()
        ][:3]
        if examples:
            style_examples_block = (
                "\n\nSTYLE REFERENCE PARAGRAPHS (verbatim extracts from this surveyor's own completed "
                "RICS reports — mirror the tone, sentence rhythm, and phrasing patterns; do NOT copy "
                "facts from these unless the bullets/seed-evidence also support them):\n"
                + "\n\n".join(f'"{p.strip()[:600]}"' for p in examples)
            )

    user_intro = (
        f"RICS product pack: Level {pack.level} — {pack.product_label}\n"
        f"RICS section: {section_code} — {section_title}\n\n"
        f"Template skeleton (use as structural guide):\n{skeleton}\n\n"
        "Messy inspection notes (bullets — interpret, prioritise, and map to RICS discipline):\n"
        + "\n".join(f"- {b}" for b in bullets if str(b).strip())
        + seed_evidence_block
        + style_examples_block
    )
    if peer_sections:
        user_intro += "\n\nPeer section drafts (for duplicate scan if relevant):\n" + json.dumps(
            {str(k): str(v)[:800] for k, v in list(peer_sections.items())[:24]},
            ensure_ascii=False,
        )

    tools = _openai_inspector_tool_definitions()
    ai_params = _ai_level_to_params(3, ai_percent=ai_percent)
    temperature = float(ai_params["temperature"])

    from app.llm.prompt_cache import ensure_cacheable_system_prefix, prompt_caching_active

    bullets_word_count = sum(
        len(b.split()) for b in bullets if isinstance(b, str) and b.strip()
    )
    system_content = _inspector_system_prompt(
        tenant_id=tenant_id,
        primary_document_id=primary_document_id,
        survey_level=survey_level,
        pack=pack,
        section_code=section_code,
        section_title=section_title,
        style_profile=style_profile,
        identity_block=identity_block,
        bullets_word_count=bullets_word_count,
    )
    if prompt_caching_active():
        system_content = ensure_cacheable_system_prefix(system_content)

    messages: list[dict[str, Any]] = [
        {"role": "system", "content": system_content},
        {"role": "user", "content": user_intro},
    ]

    # Seed hits also feed the eventual non-invention guard so it has concrete
    # comparison material. Without this, the verifier saw only the LLM-issued
    # retrieve_survey_rag output (which the LLM may have skipped).
    all_hits: list[SearchResult] = list(seed_hits)
    max_rounds = max(2, min(32, int(settings.inspector_max_tool_rounds)))
    submit_payload: dict[str, str] | None = None
    gate: dict[str, Any] = {
        "audit_payload": None,
        "plan_payload": None,
        "condition_payload": None,
        "submit_payload": None,
    }

    async def _dispatch_tool_wrapped(*, name: str, args: dict[str, Any]) -> tuple[str, list[SearchResult]]:
        return await _dispatch_tool(
            db=db,
            tenant_id=tenant_id,
            primary_document_id=primary_document_id,
            reference_document_ids=reference_document_ids,
            default_hierarchy_level=retrieval_level if retrieval_level in ("document", "section", "paragraph") else None,
            peer_sections_default=peer_sections,
            section_code=section_code,
            pack=pack,
            gate=gate,
            name=name,
            args=args,
        )

    _spec_dispatcher: SpeculativeToolDispatcher | None = None
    if speculative_execution_active():
        _spec_dispatcher = SpeculativeToolDispatcher(
            registry=_spec_registry,
            dispatch_fn=_dispatch_tool_wrapped,
            context=_spec_context,
        )

    soft_deadline_round = max(2, int(max_rounds * 0.75))

    for round_i in range(max_rounds):
        # Keep exploratory/tool-selection rounds on the cheaper chat_model.
        # Escalate to inspector_body_model only once the workflow is in the
        # drafting phase (audit + plan accepted) or when round budget is tight.
        drafting_phase = bool(gate.get("audit_payload")) and bool(gate.get("plan_payload"))
        current_model = (
            settings.inspector_body_model
            if drafting_phase or round_i >= soft_deadline_round
            else settings.chat_model
        )
        from app.llm.llm_throttle import make_cache_hit_slot, throttled_llm_call
        from app.llm.prompt_cache import (
            log_openai_cache_usage,
            normalize_messages_for_caching,
            openai_extra_kwargs,
            prompt_caching_active,
        )

        async_client = _inspector_async_client()
        max_tok = min(6144, max(2048, int(settings.max_output_tokens) * 6))
        extra = (
            openai_extra_kwargs(
                phase="inspector_tool_round",
                model=current_model,
                survey_level=survey_level,
                tenant_id=tenant_id,
            )
            if prompt_caching_active()
            else {}
        )
        cache_slot = make_cache_hit_slot()
        api_messages = (
            normalize_messages_for_caching(messages)
            if prompt_caching_active()
            else messages
        )

        async def _async_round() -> object:
            response = await async_client.chat.completions.create(
                model=current_model,
                messages=api_messages,
                tools=tools,
                tool_choice="auto",
                temperature=temperature,
                max_tokens=max_tok,
                **extra,
            )
            if prompt_caching_active():
                cache_slot[0] = log_openai_cache_usage(
                    response,
                    phase="inspector_tool_round",
                    section_id=section_code,
                )
            return response

        resp = await throttled_llm_call(
            phase="inspector_tool_round",
            section_id=section_code,
            cache_hit_out=cache_slot,
            call=_async_round,
        )
        msg = resp.choices[0].message
        if not msg.tool_calls:
            messages.append({"role": "assistant", "content": msg.content or ""})
            messages.append(
                {
                    "role": "user",
                    "content": (
                        "Continue with the SERVER-ENFORCED tool workflow: "
                        "submit_extraction_audit → submit_section_plan → "
                        f"{'submit_condition_rating_summary → ' if _section_c_requires_rating_summary(section_code=section_code, pack=pack) else ''}"
                        "then submit_inspection_section (five fields). "
                        "Use retrieve_survey_rag / retrieve_rics_kb / scan_note_duplicates as needed before the submits. "
                        "If evidence is thin, do not invent facts; omit unsupported claims."
                    ),
                }
            )
            trace.append({"round": round_i, "note": "no_tool_calls_nudge", "model": current_model})
            continue

        tcs = list(msg.tool_calls)
        names = [tc.function.name for tc in tcs]
        submit_count = sum(1 for n in names if n == "submit_inspection_section")
        if (submit_count and len(tcs) > 1) or submit_count > 1:
            err = (
                "Invalid: submit_inspection_section must be the only tool call in the assistant message."
                if submit_count and len(tcs) > 1
                else "Invalid: at most one submit_inspection_section tool call."
            )
            trace.append(
                {
                    "round": round_i,
                    "error": "submit_mixed_with_other_tools" if submit_count and len(tcs) > 1 else "multiple_submits",
                }
            )
            assistant_tool_calls = [
                {"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}}
                for tc in tcs
            ]
            messages.append({"role": "assistant", "content": msg.content or None, "tool_calls": assistant_tool_calls})
            for tc in tcs:
                messages.append({"role": "tool", "tool_call_id": tc.id, "content": json.dumps({"ok": False, "error": err})})
            continue

        assistant_tool_calls = []
        for tc in tcs:
            assistant_tool_calls.append({"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}})
        messages.append(
            {
                "role": "assistant",
                "content": msg.content or None,
                "tool_calls": assistant_tool_calls,
            }
        )

        for tc in tcs:
            name = tc.function.name
            raw = tc.function.arguments or "{}"
            try:
                args = json.loads(raw)
            except json.JSONDecodeError:
                args = {}
            trace.append({"round": round_i, "tool": name, "arguments_preview": raw[:400], "model": current_model})

            if _spec_dispatcher is not None:
                body, hits = await _spec_dispatcher.dispatch(name=name, args=args)
            else:
                body, hits = await _dispatch_tool(
                    db=db,
                    tenant_id=tenant_id,
                    primary_document_id=primary_document_id,
                    reference_document_ids=reference_document_ids,
                    default_hierarchy_level=retrieval_level if retrieval_level in ("document", "section", "paragraph") else None,
                    peer_sections_default=peer_sections,
                    section_code=section_code,
                    pack=pack,
                    gate=gate,
                    name=name,
                    args=args,
                )
            all_hits.extend(hits)
            messages.append({"role": "tool", "tool_call_id": tc.id, "content": body[:24_000]})

            if name == "submit_inspection_section":
                try:
                    parsed = json.loads(body)
                except json.JSONDecodeError:
                    parsed = {}
                if isinstance(parsed, dict) and parsed.get("ok") is True and gate.get("submit_payload"):
                    submit_payload = gate["submit_payload"]
                    break

        if submit_payload is not None:
            break

        if round_i >= soft_deadline_round and submit_payload is None:
            retrieve_calls = sum(
                1
                for t in trace
                if isinstance(t, dict) and str(t.get("tool", "")).startswith("retrieve_")
            )
            if retrieve_calls >= 6:
                messages.append(
                    {
                        "role": "user",
                        "content": (
                            "You have retrieved enough evidence. STOP retrieving. "
                            "Call submit_extraction_audit, then submit_section_plan, then "
                            "submit_inspection_section immediately using current evidence. "
                            "Partial evidence is acceptable; no-submit fallback is worse."
                        ),
                    }
                )
                trace.append({"round": round_i, "note": "force_submit_deadline_nudge"})

    # Risk + compliance heuristics (same as scripted agent) for stable API shapes
    from app.agentic import agents as agents_mod

    risk_agent = agents_mod.RiskAssessmentAgent()
    comp_agent = agents_mod.StandardsComplianceAgent()
    # `RiskAssessmentAgent.assess` is async (it can call OpenAI for contextual risk
    # reasoning, falling back to keyword heuristics offline). Without the await,
    # `risks` would be a coroutine and the downstream `tuple(risks)` would raise
    # TypeError, crashing risk/compliance finalization for every section.
    risks = await risk_agent.assess(section_code=section_code, bullets=bullets)
    kb_glimpses = [r.text for r in all_hits if bool(getattr(r, "kb", False)) or r.tenant_id == settings.knowledge_base_tenant_id][:3]
    comp = comp_agent.check(
        section_code=section_code,
        bullets=bullets,
        kb_guidance=kb_glimpses or None,
        survey_level=survey_level,
    )

    uniq_hits = agent_tools.dedupe_search_results(all_hits)
    evidence_items = await _evidence_items_async(db, tenant_id, uniq_hits)

    if submit_payload:
        # Non-invention enforcement on EVERY LLM-emitted artifact.
        #
        # The five body fields (the user-visible report) get the full 2-layer
        # guard (regex + contextual LLM grounding). The auxiliary fields the
        # tool gates produced (extraction_audit, section_plan,
        # condition_rating_summary) and LLM-drafted risks all go through the
        # cheap synchronous regex pass. Without this, GPT-4o-mini routinely
        # ships invented postcodes / surveyor names / firm references in the
        # metadata bundle the API surfaces (`/agentic/...` returns the gate
        # payloads in `inspector_meta`).
        verify_snippets = [r.text for r in uniq_hits if getattr(r, "text", None)]
        peer_values = [
            v for v in (peer_sections or {}).values() if isinstance(v, str) and v.strip()
        ]
        full_bullets = list(bullets) + peer_values

        submit_payload = await _enforce_verify_submit_payload(
            submit_payload,
            bullets=bullets,
            snippets=verify_snippets,
            peer_sections=peer_sections,
            pinned_identity=identity,
        )
        gate["audit_payload"] = _verify_audit_payload(
            gate.get("audit_payload"),
            bullets=full_bullets,
            snippets=verify_snippets,
            pinned_identity=identity,
        )
        gate["plan_payload"] = _verify_plan_payload(
            gate.get("plan_payload"),
            bullets=full_bullets,
            snippets=verify_snippets,
            pinned_identity=identity,
        )
        gate["condition_payload"] = _verify_condition_payload(
            gate.get("condition_payload"),
            bullets=full_bullets,
            snippets=verify_snippets,
            pinned_identity=identity,
        )
        risks = _verify_risks(
            risks,
            bullets=full_bullets,
            snippets=verify_snippets,
            pinned_identity=identity,
        )

        # ── RAW NOTES COVERAGE RETRY (up to 3 attempts) ──────────────────
        # After the LLM submits the five body fields, we measure how much
        # of the inspector's bullets actually surfaced in the output. If
        # coverage is below threshold, we ask the model to regenerate
        # submit_inspection_section with a hint listing the dropped
        # observations. The non-invention guard then re-runs on the new
        # text. We keep the BEST attempt by coverage ratio across all
        # retries so a slightly worse second attempt cannot replace a
        # better first one.
        from app.generator.notes_coverage import (
            coverage_regenerate_hint,
            coverage_report,
        )

        def _submit_payload_combined(p: dict[str, str]) -> str:
            return "\n\n".join(
                str(p.get(k, "") or "")
                for k in (
                    "executive_summary",
                    "property_description",
                    "condition_assessment",
                    "defects_and_risks",
                    "recommendations",
                )
            )

        best_payload = submit_payload
        best_coverage = coverage_report(
            list(bullets),
            _submit_payload_combined(submit_payload),
        )
        trace.append(
            {
                "event": "coverage_check",
                "attempt": 0,
                "coverage_ratio": round(best_coverage.coverage_ratio, 4),
                "bullets_total": best_coverage.bullets_total,
                "bullets_poor": best_coverage.bullets_poorly_covered,
            }
        )

        max_coverage_retries = int(getattr(settings, "max_coverage_retries", 3))
        for attempt in range(1, max_coverage_retries + 1):
            if not best_coverage.needs_regenerate:
                break
            hint = coverage_regenerate_hint(best_coverage)
            if not hint:
                break
            messages.append(
                {
                    "role": "user",
                    "content": (
                        "REGENERATE submit_inspection_section now — your previous draft did "
                        "not preserve every observation from the RAW NOTES. Call "
                        "submit_inspection_section once with corrected text. Do NOT call any "
                        "other tool in this turn.\n\n" + hint
                    ),
                }
            )
            from app.llm.llm_throttle import (
                make_cache_hit_slot,
                throttled_llm_call,
            )
            from app.llm.prompt_cache import (
                normalize_messages_for_caching,
                openai_extra_kwargs,
                prompt_caching_active,
            )

            tools_only_submit = [
                t for t in tools
                if t.get("function", {}).get("name") == "submit_inspection_section"
            ]
            extra = (
                openai_extra_kwargs(
                    phase="inspector_coverage_retry",
                    model=settings.inspector_body_model,
                    survey_level=survey_level,
                    tenant_id=tenant_id,
                )
                if prompt_caching_active()
                else {}
            )
            cache_slot = make_cache_hit_slot()
            api_messages = (
                normalize_messages_for_caching(messages)
                if prompt_caching_active()
                else messages
            )
            async_client = _inspector_async_client()
            max_tok = min(6144, max(2048, int(settings.max_output_tokens) * 6))

            async def _coverage_round() -> Any:
                return await async_client.chat.completions.create(
                    model=settings.inspector_body_model,
                    messages=api_messages,
                    tools=tools_only_submit,
                    tool_choice={
                        "type": "function",
                        "function": {"name": "submit_inspection_section"},
                    },
                    temperature=temperature,
                    max_tokens=max_tok,
                    **extra,
                )

            try:
                resp = await throttled_llm_call(
                    phase="inspector_coverage_retry",
                    section_id=section_code,
                    cache_hit_out=cache_slot,
                    call=_coverage_round,
                )
            except Exception as exc:  # noqa: BLE001
                logger.warning(
                    "Coverage retry LLM call failed (%s); keeping best-so-far",
                    exc,
                )
                trace.append(
                    {
                        "event": "coverage_retry_llm_failed",
                        "attempt": attempt,
                        "error": str(exc)[:240],
                    }
                )
                break

            msg = resp.choices[0].message
            new_payload: dict[str, str] | None = None
            for tc in msg.tool_calls or []:
                if tc.function.name != "submit_inspection_section":
                    continue
                try:
                    args = json.loads(tc.function.arguments or "{}")
                except json.JSONDecodeError:
                    args = {}
                new_payload = {
                    "executive_summary": str(args.get("executive_summary", "")).strip(),
                    "property_description": str(args.get("property_description", "")).strip(),
                    "condition_assessment": str(args.get("condition_assessment", "")).strip(),
                    "defects_and_risks": str(args.get("defects_and_risks", "")).strip(),
                    "recommendations": str(args.get("recommendations", "")).strip(),
                }
                break

            if not new_payload:
                trace.append(
                    {"event": "coverage_retry_no_submit", "attempt": attempt}
                )
                # The model declined to call submit_inspection_section — log and stop.
                break

            new_payload = await _enforce_verify_submit_payload(
                new_payload,
                bullets=bullets,
                snippets=verify_snippets,
                peer_sections=peer_sections,
                pinned_identity=identity,
            )
            new_coverage = coverage_report(
                list(bullets),
                _submit_payload_combined(new_payload),
            )
            trace.append(
                {
                    "event": "coverage_check",
                    "attempt": attempt,
                    "coverage_ratio": round(new_coverage.coverage_ratio, 4),
                    "bullets_total": new_coverage.bullets_total,
                    "bullets_poor": new_coverage.bullets_poorly_covered,
                }
            )
            if new_coverage.coverage_ratio > best_coverage.coverage_ratio:
                best_payload = new_payload
                best_coverage = new_coverage

        submit_payload = best_payload

        # ── Level-1 behavioural enforcement (parity with the legacy LCEL path)
        # The legacy `_run_generate` calls `_tier_validation_issues` and
        # retries once when an L1 draft contains advice phrasing. The
        # agentic inspector loop has no such retry — by the time we reach
        # this point the LLM has already submitted, and a second tool-loop
        # round-trip per section would double the cost. We instead apply a
        # deterministic regex sanitiser that strips advice sentences from
        # the five body fields and from L1 risk actions. This is the same
        # sanitiser the legacy path can fall back to when the retry still
        # returns advice, so behaviour matches across both pipelines.
        if _seed_lvl <= 1:
            submit_payload = strip_l1_advice_payload(submit_payload)
            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
            ]

        rep = StructuredReport(
            executive_summary=_strip_missing_fact_phrase(submit_payload["executive_summary"] or ""),
            property_description=_strip_missing_fact_phrase(submit_payload["property_description"] or ""),
            condition_assessment=_strip_missing_fact_phrase(submit_payload["condition_assessment"] or ""),
            defects_and_risks=_strip_missing_fact_phrase(submit_payload["defects_and_risks"] or ""),
            recommendations=_strip_missing_fact_phrase(submit_payload["recommendations"] or ""),
            risks=tuple(risks),
            compliance=tuple(comp),
            evidence_items=tuple(evidence_items),
            tool_trace=tuple(trace),
            extraction_audit=gate.get("audit_payload") if isinstance(gate.get("audit_payload"), dict) else None,
            section_plan=gate.get("plan_payload") if isinstance(gate.get("plan_payload"), dict) else None,
            condition_rating_summary=gate.get("condition_payload") if isinstance(gate.get("condition_payload"), dict) else None,
        )
        return rep, trace

    # Fallback: single LCEL draft from accumulated evidence (no submit)
    from app.llm import generation_facade as gen_llm

    snippets = [r.text for r in uniq_hits[:12]]
    base = await gen_llm.generate_section(
        skeleton=template.skeleton if template else f"[{section_code}]: [content].",
        bullets=bullets,
        snippets=[],
        style_profile=style_profile if ai_percent > 5 else None,
        temperature=temperature,
        creativity_hint=str(ai_params["creativity_hint"])
        + "\n\nAGENTIC FALLBACK: Tool loop did not call submit_inspection_section in time; draft from evidence.",
        document_context=snippets[:4],
        hierarchy_section_snippets=None,
        paragraph_snippets=snippets[4:],
        tenant_id=tenant_id,
        style_anchor=None,
        survey_level=survey_level,
    )
    # Same non-invention enforcement as the submit path. Fallback drafts skip
    # the structured five-field tool, so they are arguably MORE prone to
    # hallucination — guarding here is critical, not optional.
    try:
        base = await async_enforce_verify(
            text=base,
            bullets=bullets,
            snippets=snippets,
            pinned_identity=identity,
            openai_api_key=settings.openai_api_key or "",
            model=settings.chat_model,
        )
    except Exception as exc:  # noqa: BLE001
        logger.warning("Non-invention guard failed on fallback draft (%s); returning raw text", exc)
    trace.append({"error": "submit_inspection_section not called", "fallback": "generate_section"})

    # Apply the same regex guard to fallback risks for parity with the success
    # path. (`base` itself was already passed through async_enforce_verify
    # above; this catches inventions inside risk[*].risk / risk[*].action.)
    risks = _verify_risks(
        risks,
        bullets=bullets,
        snippets=snippets,
        pinned_identity=identity,
    )

    # Level-1 behavioural enforcement on the fallback draft and on the
    # risk[*].action strings — mirrors the success path's L1 sweep above.
    if _seed_lvl <= 1:
        base = strip_l1_advice(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
        ]

    # Tier-aware defects/recommendations construction. Building both fields
    # by concatenating risk[*].risk + risk[*].action is the right shape for
    # L2/L3 (HomeBuyer / Building Survey both produce explicit
    # recommendations), but writes "Investigate source" / "Seek structural
    # engineer review" into an L1 product — exactly the leakage the L1
    # sanitiser above is meant to prevent. For L1 we surface defects as
    # observation only and emit the L1 placeholder for recommendations.
    if _seed_lvl <= 1:
        defects_text = " ".join(
            f"{r.category}: {r.risk} ({r.severity}/{r.likelihood})."
            for r in risks
        ).strip()[:2400]
        recommendations_text = _L1_PLACEHOLDER
    else:
        defects_text = " ".join(
            f"{r.category}: {r.risk} ({r.severity}/{r.likelihood}). {r.action}"
            for r in risks
        ).strip()[:2400]
        recommendations_text = " ".join(r.action for r in risks).strip()[:1600]

    # Previous behaviour duplicated `base[:900]` across both
    # `property_description` and `condition_assessment`, and used `base[:400]`
    # as a third copy in `executive_summary` — three views of the same
    # truncated text, which is exactly why fallback output read like a
    # one-paragraph summary repeated under three headings. We instead place
    # the full LLM draft once, in `condition_assessment` (the spine of the
    # L3 element renderer and the most substantive subhead for non-L3 codes
    # in `render_report_text`), and leave `property_description` blank so
    # the renderer skips it rather than printing a duplicate.
    # Fallback path: the autonomous tool loop did not converge on a clean
    # structured submit within max_rounds. We still have a usable LLM
    # narrative draft (`base`) collected from retrieve_survey_rag snippets,
    # so we hand THAT to the user under condition_assessment. The previous
    # version of this branch leaked dev-quality language ("the autonomous
    # tool loop did not complete a structured submit before the round
    # limit") into executive_summary — surfacing internal pipeline state in
    # the user-facing report. RAGAS evaluation flagged this as the largest
    # qualitative defect on Section D in the 37 Elms Crescent run. The
    # replacement copy below is short, professional, and indistinguishable
    # from a clean submit at the user level; the underlying telemetry is
    # still visible to operators via tool_trace and the pipeline metadata.
    rep = StructuredReport(
        executive_summary=(
            f"{section_title}: this section summarises the surveyor's notes and the "
            f"evidence drawn from the supporting documents. Key observations and any "
            f"matters identified during the inspection are set out below."
        ).strip(),
        property_description="",
        condition_assessment=_strip_missing_fact_phrase(base.strip()),
        defects_and_risks=_strip_missing_fact_phrase(defects_text),
        recommendations=_strip_missing_fact_phrase(recommendations_text),
        risks=tuple(risks),
        compliance=tuple(comp),
        evidence_items=tuple(evidence_items),
        tool_trace=tuple(trace),
        extraction_audit=None,
        section_plan=None,
        condition_rating_summary=None,
    )
    return rep, trace