Reflection Agent — Python (повна реалізація з LLM)

examples/
└── agent-patterns/
    └── reflection-agent/
        └── python/
            ├── main.py          # Draft -> Review -> Revise -> Finalize flow
            ├── llm.py           # draft/review/revise LLM calls
            ├── gateway.py       # policy+execution validation, patch guards
            ├── context.py       # deterministic incident context
            ├── requirements.txt
            └── README.md

git clone https://github.com/AgentPatterns-tech/agentpatterns.git
cd agentpatterns

cd examples/agent-patterns/reflection-agent/python
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

export OPENAI_API_KEY="sk-..."
# optional:
# export OPENAI_MODEL="gpt-4.1-mini"
# export OPENAI_TIMEOUT_SECONDS="60"

python main.py

from __future__ import annotations

from typing import Any


def build_incident_context(*, report_date: str, region: str) -> dict[str, Any]:
    return {
        "report_date": report_date,
        "region": region,
        "incident": {
            "incident_id": "inc_payments_20260305",
            "severity": "P1",
            "status": "degraded",
            "affected_checkout_pct": 27,
            "failed_payment_rate": 0.034,
            "chargeback_alerts": 5,
            "eta_minutes": 45,
        },
        "policy_hints": {
            "avoid_absolute_guarantees": True,
            "required_sections": ["current_status", "customer_impact", "next_actions"],
        },
        "approved_actions": [
            "monitor payment failures every 15 minutes",
            "publish customer update via status page",
            "prepare support macro with workaround guidance",
        ],
    }

from __future__ import annotations

import hashlib
import json
import re
from dataclasses import dataclass
from difflib import SequenceMatcher
from typing import Any


class StopRun(Exception):
    def __init__(self, reason: str):
        super().__init__(reason)
        self.reason = reason


@dataclass(frozen=True)
class Budget:
    max_seconds: int = 30
    max_draft_chars: int = 900
    max_review_issues: int = 4
    max_fix_items: int = 4
    max_answer_chars: int = 900
    min_patch_similarity: float = 0.45


NUMBER_TOKEN_RE = re.compile(r"\b\d+(?:\.\d+)?%?\b")
INCIDENT_ID_RE = re.compile(r"\binc_[a-z0-9_]+\b", re.IGNORECASE)
SEVERITY_RE = re.compile(r"\bp[0-5]\b", re.IGNORECASE)
REGION_RE = re.compile(r"\b(us|eu|uk|ua|apac|global|emea|latam)\b", re.IGNORECASE)
QUOTED_PHRASE_RE = re.compile(r"['\"]([^'\"]{3,120})['\"]")

RESTRICTED_CLAIMS_RE = [
    re.compile(r"\bresolved\b", re.IGNORECASE),
    re.compile(r"\bfully[-\s]+recovered\b", re.IGNORECASE),
    re.compile(r"\bincident\s+closed\b", re.IGNORECASE),
    re.compile(r"\ball payments (?:are|is)\s+stable\b", re.IGNORECASE),
]


def _stable_json(value: Any) -> str:
    if value is None or isinstance(value, (bool, int, float, str)):
        return json.dumps(value, ensure_ascii=True, sort_keys=True)
    if isinstance(value, list):
        return "[" + ",".join(_stable_json(v) for v in value) + "]"
    if isinstance(value, dict):
        parts = []
        for key in sorted(value):
            parts.append(json.dumps(str(key), ensure_ascii=True) + ":" + _stable_json(value[key]))
        return "{" + ",".join(parts) + "}"
    return json.dumps(str(value), ensure_ascii=True)


def _normalize_space(text: str) -> str:
    return " ".join((text or "").strip().split())


def text_hash(text: str) -> str:
    normalized = _normalize_space(text)
    raw = _stable_json(normalized)
    return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:12]


def _extract_number_tokens(text: str) -> set[str]:
    normalized = _normalize_space(text).lower()
    return set(NUMBER_TOKEN_RE.findall(normalized))


def _extract_incident_ids(text: str) -> set[str]:
    normalized = _normalize_space(text).lower()
    return set(INCIDENT_ID_RE.findall(normalized))


def _extract_severity_labels(text: str) -> set[str]:
    normalized = _normalize_space(text).upper()
    return {match.upper() for match in SEVERITY_RE.findall(normalized)}


def _extract_regions(text: str) -> set[str]:
    normalized = _normalize_space(text).upper()
    return {value.upper() for value in REGION_RE.findall(normalized)}


def _extract_fix_plan_phrase_rules(fix_plan: list[str]) -> dict[str, list[str]]:
    must_include: list[str] = []
    must_remove: list[str] = []

    def _append_unique(target: list[str], value: str) -> None:
        if value and value not in target:
            target.append(value)

    for item in fix_plan:
        item_norm = _normalize_space(item).lower()
        quoted = [_normalize_space(match).lower() for match in QUOTED_PHRASE_RE.findall(item)]
        quoted = [value for value in quoted if value]
        if not quoted:
            continue

        is_replace = "replace" in item_norm
        is_modify = any(word in item_norm for word in ("modify", "change", "update", "rewrite"))
        has_with = " with " in f" {item_norm} "
        has_example_marker = any(
            marker in item_norm for marker in ("such as", "for example", "e.g.", "e.g")
        )

        if is_replace or is_modify:
            _append_unique(must_remove, quoted[0])

            # Enforce phrase add only for strict replace-with instructions, not modify/example hints.
            if is_replace and len(quoted) >= 2 and has_with and not has_example_marker:
                _append_unique(must_include, quoted[1])
            continue

        for phrase in quoted:
            _append_unique(must_include, phrase)

    return {
        "must_include": must_include,
        "must_remove": must_remove,
    }


def _context_claim_text(value: Any) -> str:
    if value is None:
        return ""
    if isinstance(value, str):
        return value
    if isinstance(value, (bool, int, float)):
        return str(value)
    if isinstance(value, list):
        return " ".join(_context_claim_text(item) for item in value)
    if isinstance(value, dict):
        parts: list[str] = []
        for key, item in value.items():
            parts.append(str(key))
            parts.append(_context_claim_text(item))
        return " ".join(parts)
    return str(value)


def _is_high_risk_issue(issue_type: str) -> bool:
    return issue_type in {"legal_risk", "policy_violation"}


def validate_draft(draft: Any, *, max_chars: int) -> str:
    if not isinstance(draft, str) or not draft.strip():
        raise StopRun("invalid_draft:empty")
    normalized = draft.strip()
    if len(normalized) > max_chars:
        raise StopRun("invalid_draft:too_long")
    return normalized


def validate_review(
    raw: Any,
    *,
    allowed_decisions_policy: set[str],
    allowed_issue_types_policy: set[str],
    max_review_issues: int,
    max_fix_items: int,
) -> dict[str, Any]:
    if not isinstance(raw, dict):
        raise StopRun("invalid_review:not_object")

    decision = raw.get("decision")
    if not isinstance(decision, str) or not decision.strip():
        raise StopRun("invalid_review:decision")
    decision = decision.strip()
    if decision not in allowed_decisions_policy:
        raise StopRun(f"review_decision_not_allowed_policy:{decision}")

    issues_raw = raw.get("issues", [])
    if not isinstance(issues_raw, list):
        raise StopRun("invalid_review:issues")
    if len(issues_raw) > max_review_issues:
        raise StopRun("invalid_review:too_many_issues")

    issues: list[dict[str, str]] = []
    for item in issues_raw:
        if not isinstance(item, dict):
            raise StopRun("invalid_review:issue_item")

        issue_type = item.get("type")
        note = item.get("note")

        if not isinstance(issue_type, str) or not issue_type.strip():
            raise StopRun("invalid_review:issue_type")
        issue_type = issue_type.strip()
        if issue_type not in allowed_issue_types_policy:
            raise StopRun(f"review_issue_not_allowed_policy:{issue_type}")

        if not isinstance(note, str) or not note.strip():
            raise StopRun("invalid_review:issue_note")

        issues.append({"type": issue_type, "note": note.strip()})

    fix_plan_raw = raw.get("fix_plan", [])
    if not isinstance(fix_plan_raw, list):
        raise StopRun("invalid_review:fix_plan")
    if len(fix_plan_raw) > max_fix_items:
        raise StopRun("invalid_review:too_many_fix_items")

    fix_plan: list[str] = []
    for item in fix_plan_raw:
        if not isinstance(item, str) or not item.strip():
            raise StopRun("invalid_review:fix_item")
        fix_plan.append(item.strip())

    reason = raw.get("reason", "")
    if reason is None:
        reason = ""
    if not isinstance(reason, str):
        raise StopRun("invalid_review:reason")
    reason = reason.strip()

    if decision == "approve":
        if issues and any(_is_high_risk_issue(issue["type"]) for issue in issues):
            raise StopRun("invalid_review:approve_with_high_risk_issue")
        return {
            "decision": "approve",
            "issues": issues,
            "fix_plan": [],
            "reason": reason,
            "high_risk": False,
        }

    if decision == "revise":
        if not issues:
            raise StopRun("invalid_review:revise_without_issues")
        if not fix_plan:
            raise StopRun("invalid_review:revise_without_fix_plan")
        if any(_is_high_risk_issue(issue["type"]) for issue in issues):
            raise StopRun("invalid_review:high_risk_requires_escalate")
        return {
            "decision": "revise",
            "issues": issues,
            "fix_plan": fix_plan,
            "reason": reason,
            "high_risk": False,
        }

    if decision == "escalate":
        if not reason:
            raise StopRun("invalid_review:escalate_reason_required")
        return {
            "decision": "escalate",
            "issues": issues,
            "fix_plan": [],
            "reason": reason,
            "high_risk": True,
        }

    raise StopRun("invalid_review:unknown_decision")


class ReflectionGateway:
    def __init__(
        self,
        *,
        allow_execution_decisions: set[str],
        budget: Budget,
    ):
        self.allow_execution_decisions = set(allow_execution_decisions)
        self.budget = budget

    def enforce_execution_decision(self, decision: str) -> None:
        if decision not in self.allow_execution_decisions:
            raise StopRun(f"review_decision_denied_execution:{decision}")

    def validate_revision(
        self,
        *,
        original: str,
        revised: str,
        context: dict[str, Any],
        fix_plan: list[str] | None = None,
    ) -> dict[str, Any]:
        if not isinstance(revised, str) or not revised.strip():
            raise StopRun("invalid_revised:empty")

        revised_clean = revised.strip()
        if len(revised_clean) > self.budget.max_answer_chars:
            raise StopRun("invalid_revised:too_long")

        normalized_original = _normalize_space(original)
        normalized_revised = _normalize_space(revised_clean)
        if normalized_original == normalized_revised:
            raise StopRun("invalid_revised:no_changes")

        similarity = SequenceMatcher(a=normalized_original, b=normalized_revised).ratio()
        if similarity < self.budget.min_patch_similarity:
            raise StopRun("patch_violation:too_large_edit")

        allowed_text_tokens = _stable_json(context) + " " + original
        allowed_text_claims = _normalize_space(_context_claim_text(context) + " " + original)
        revised_numbers = _extract_number_tokens(revised_clean)
        allowed_numbers = _extract_number_tokens(allowed_text_tokens)
        if revised_numbers - allowed_numbers:
            raise StopRun("patch_violation:no_new_facts")

        revised_ids = _extract_incident_ids(revised_clean)
        allowed_ids = _extract_incident_ids(allowed_text_tokens)
        if revised_ids - allowed_ids:
            raise StopRun("patch_violation:new_incident_id")

        revised_severity = _extract_severity_labels(revised_clean)
        allowed_severity = _extract_severity_labels(allowed_text_tokens)
        if revised_severity - allowed_severity:
            raise StopRun("patch_violation:new_severity_label")

        revised_regions = _extract_regions(revised_clean)
        allowed_regions = _extract_regions(allowed_text_tokens)
        if revised_regions - allowed_regions:
            raise StopRun("patch_violation:new_region")

        for claim_re in RESTRICTED_CLAIMS_RE:
            if claim_re.search(revised_clean) and not claim_re.search(allowed_text_claims):
                raise StopRun("patch_violation:restricted_claims")

        phrase_rules = _extract_fix_plan_phrase_rules(fix_plan or [])
        must_include = phrase_rules["must_include"]
        must_remove = phrase_rules["must_remove"]
        if must_include or must_remove:
            revised_lower = _normalize_space(revised_clean).lower()
            missing = [phrase for phrase in must_include if phrase not in revised_lower]
            if missing:
                raise StopRun("patch_violation:fix_plan_not_applied")
            still_present = [phrase for phrase in must_remove if phrase in revised_lower]
            if still_present:
                raise StopRun("patch_violation:fix_plan_not_applied")

        return {
            "answer": revised_clean,
            "patch_similarity": round(similarity, 3),
            "fix_plan_quoted_checks": len(must_include) + len(must_remove),
        }

    def validate_final(self, answer: str) -> str:
        if not isinstance(answer, str) or not answer.strip():
            raise StopRun("invalid_answer:empty")

        cleaned = answer.strip()
        if len(cleaned) > self.budget.max_answer_chars:
            raise StopRun("invalid_answer:too_long")
        return cleaned

from __future__ import annotations

import json
import os
from typing import Any

from openai import APIConnectionError, APITimeoutError, OpenAI

MODEL = os.getenv("OPENAI_MODEL", "gpt-4.1-mini")
LLM_TIMEOUT_SECONDS = float(os.getenv("OPENAI_TIMEOUT_SECONDS", "60"))


class LLMTimeout(Exception):
    pass


class LLMEmpty(Exception):
    pass


class LLMInvalid(Exception):
    pass


DRAFT_SYSTEM_PROMPT = """
You are an operations communications writer.
Return exactly one JSON object:
{
  "draft": "short customer-safe incident update"
}

Rules:
- Use only facts from provided incident_context.
- Include current status, customer impact, and next actions.
- Avoid absolute guarantees and overconfident claims.
- Keep draft concise and actionable.
- Do not output markdown or extra keys.
""".strip()

REVIEW_SYSTEM_PROMPT = """
You are a reflection reviewer.
Return exactly one JSON object:
{
  "decision": "approve|revise|escalate",
  "issues": [{"type":"overconfidence","note":"..."}],
  "fix_plan": ["patch instruction"],
  "reason": "for escalate only"
}

Rules:
- Review exactly once.
- decision=approve: fix_plan must be empty.
- decision=revise: provide 1-4 concrete patch-only instructions.
- For enforceable instructions, include quoted target phrases in fix_plan.
- decision=escalate: use only for high-risk or policy-unsafe content.
- Do not add new facts in fix_plan.
- Use only issue types from allowed_issue_types.
- Do not output markdown or extra keys.
""".strip()

REVISE_SYSTEM_PROMPT = """
You are an editor applying one controlled patch.
Return exactly one JSON object:
{
  "revised_answer": "updated answer"
}

Rules:
- Edit only what is needed to satisfy fix_plan.
- Keep scope and intent of original draft.
- Do not introduce new facts or numbers.
- Keep answer concise and customer-safe.
- Do not output markdown or extra keys.
""".strip()


def _get_client() -> OpenAI:
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        raise EnvironmentError(
            "OPENAI_API_KEY is not set. Run: export OPENAI_API_KEY='sk-...'"
        )
    return OpenAI(api_key=api_key)


def _chat_json(*, system_prompt: str, payload: dict[str, Any]) -> dict[str, Any]:
    client = _get_client()
    try:
        completion = client.chat.completions.create(
            model=MODEL,
            temperature=0,
            timeout=LLM_TIMEOUT_SECONDS,
            response_format={"type": "json_object"},
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": json.dumps(payload, ensure_ascii=False)},
            ],
        )
    except (APITimeoutError, APIConnectionError) as exc:
        raise LLMTimeout("llm_timeout") from exc

    text = completion.choices[0].message.content or "{}"
    try:
        data = json.loads(text)
    except json.JSONDecodeError as exc:
        raise LLMInvalid("llm_invalid_json") from exc

    if not isinstance(data, dict):
        raise LLMInvalid("llm_invalid_json")
    return data


def generate_draft(*, goal: str, incident_context: dict[str, Any]) -> str:
    payload = {
        "goal": goal,
        "incident_context": incident_context,
    }
    data = _chat_json(system_prompt=DRAFT_SYSTEM_PROMPT, payload=payload)

    draft = data.get("draft")
    if not isinstance(draft, str):
        raise LLMInvalid("llm_invalid_schema")

    draft = draft.strip()
    if not draft:
        raise LLMEmpty("llm_empty")
    return draft


def review_draft(
    *,
    goal: str,
    incident_context: dict[str, Any],
    draft: str,
    allowed_issue_types: list[str],
) -> dict[str, Any]:
    payload = {
        "goal": goal,
        "incident_context": incident_context,
        "draft": draft,
        "allowed_issue_types": allowed_issue_types,
    }
    return _chat_json(system_prompt=REVIEW_SYSTEM_PROMPT, payload=payload)


def revise_once(
    *,
    goal: str,
    incident_context: dict[str, Any],
    draft: str,
    fix_plan: list[str],
) -> str:
    payload = {
        "goal": goal,
        "incident_context": incident_context,
        "draft": draft,
        "fix_plan": fix_plan,
    }
    data = _chat_json(system_prompt=REVISE_SYSTEM_PROMPT, payload=payload)

    revised = data.get("revised_answer")
    if not isinstance(revised, str):
        raise LLMInvalid("llm_invalid_schema")

    revised = revised.strip()
    if not revised:
        raise LLMEmpty("llm_empty")
    return revised

from __future__ import annotations

import json
import time
import uuid
from typing import Any

from context import build_incident_context
from gateway import Budget, ReflectionGateway, StopRun, text_hash, validate_draft, validate_review
from llm import LLMEmpty, LLMInvalid, LLMTimeout, generate_draft, review_draft, revise_once

GOAL = (
    "Draft a customer-facing payment incident update for US enterprise customers. "
    "Keep it accurate, avoid overconfident language, and include next actions."
)
INCIDENT_CONTEXT = build_incident_context(report_date="2026-03-05", region="US")

BUDGET = Budget(
    max_seconds=30,
    max_draft_chars=900,
    max_review_issues=4,
    max_fix_items=4,
    max_answer_chars=900,
    min_patch_similarity=0.45,
)

ALLOWED_REVIEW_DECISIONS_POLICY = {"approve", "revise", "escalate"}
AUTO_REVISION_ENABLED = True
ALLOWED_REVIEW_DECISIONS_EXECUTION = (
    ALLOWED_REVIEW_DECISIONS_POLICY if AUTO_REVISION_ENABLED else {"approve", "escalate"}
)

ALLOWED_ISSUE_TYPES_POLICY = {
    "overconfidence",
    "missing_uncertainty",
    "contradiction",
    "scope_leak",
    "policy_violation",
    "legal_risk",
}


def run_reflection_agent(*, goal: str, incident_context: dict[str, Any]) -> dict[str, Any]:
    run_id = str(uuid.uuid4())
    started = time.monotonic()
    trace: list[dict[str, Any]] = []
    history: list[dict[str, Any]] = []

    gateway = ReflectionGateway(
        allow_execution_decisions=ALLOWED_REVIEW_DECISIONS_EXECUTION,
        budget=BUDGET,
    )

    def stopped(stop_reason: str, *, phase: str, **extra: Any) -> dict[str, Any]:
        payload = {
            "run_id": run_id,
            "status": "stopped",
            "stop_reason": stop_reason,
            "phase": phase,
            "trace": trace,
            "history": history,
        }
        payload.update(extra)
        return payload

    try:
        draft_raw = generate_draft(goal=goal, incident_context=incident_context)
        draft = validate_draft(draft_raw, max_chars=BUDGET.max_draft_chars)
    except LLMTimeout:
        return stopped("llm_timeout", phase="draft")
    except LLMInvalid as exc:
        return stopped(exc.args[0], phase="draft")
    except LLMEmpty:
        return stopped("llm_empty", phase="draft")
    except StopRun as exc:
        return stopped(exc.reason, phase="draft")

    trace.append(
        {
            "step": 1,
            "phase": "draft",
            "draft_hash": text_hash(draft),
            "chars": len(draft),
            "ok": True,
        }
    )
    history.append(
        {
            "step": 1,
            "action": "draft_once",
            "draft": draft,
        }
    )

    if (time.monotonic() - started) > BUDGET.max_seconds:
        return stopped("max_seconds", phase="review")

    try:
        raw_review = review_draft(
            goal=goal,
            incident_context=incident_context,
            draft=draft,
            allowed_issue_types=sorted(ALLOWED_ISSUE_TYPES_POLICY),
        )
    except LLMTimeout:
        return stopped("llm_timeout", phase="review")
    except LLMInvalid as exc:
        return stopped(exc.args[0], phase="review")

    try:
        review = validate_review(
            raw_review,
            allowed_decisions_policy=ALLOWED_REVIEW_DECISIONS_POLICY,
            allowed_issue_types_policy=ALLOWED_ISSUE_TYPES_POLICY,
            max_review_issues=BUDGET.max_review_issues,
            max_fix_items=BUDGET.max_fix_items,
        )
        gateway.enforce_execution_decision(review["decision"])
    except StopRun as exc:
        return stopped(exc.reason, phase="review", raw_review=raw_review)

    trace.append(
        {
            "step": 2,
            "phase": "review",
            "decision": review["decision"],
            "issues": len(review["issues"]),
            "fix_items": len(review["fix_plan"]),
            "ok": True,
        }
    )
    history.append(
        {
            "step": 2,
            "action": "review_once",
            "review": review,
        }
    )

    if review["decision"] == "escalate":
        escalation_reason = str(review.get("reason", "")).strip()
        return {
            "run_id": run_id,
            "status": "stopped",
            "stop_reason": "policy_escalation",
            "escalation_reason": escalation_reason[:120],
            "phase": "review",
            "review": review,
            "trace": trace,
            "history": history,
        }

    final_answer = draft
    revised = False

    if review["decision"] == "revise":
        if (time.monotonic() - started) > BUDGET.max_seconds:
            return stopped("max_seconds", phase="revise")

        try:
            revised_raw = revise_once(
                goal=goal,
                incident_context=incident_context,
                draft=draft,
                fix_plan=review["fix_plan"],
            )
            revised_payload = gateway.validate_revision(
                original=draft,
                revised=revised_raw,
                context=incident_context,
                fix_plan=review["fix_plan"],
            )
        except LLMTimeout:
            return stopped("llm_timeout", phase="revise")
        except LLMInvalid as exc:
            return stopped(exc.args[0], phase="revise")
        except LLMEmpty:
            return stopped("llm_empty", phase="revise")
        except StopRun as exc:
            return stopped(exc.reason, phase="revise")

        final_answer = revised_payload["answer"]
        revised = True

        trace.append(
            {
                "step": 3,
                "phase": "revise",
                "patch_similarity": revised_payload["patch_similarity"],
                "fix_plan_quoted_checks": revised_payload["fix_plan_quoted_checks"],
                "revised_hash": text_hash(final_answer),
                "ok": True,
            }
        )
        history.append(
            {
                "step": 3,
                "action": "revise_once",
                "fix_plan": review["fix_plan"],
                "revised_answer": final_answer,
            }
        )

    try:
        final_answer = gateway.validate_final(final_answer)
    except StopRun as exc:
        return stopped(exc.reason, phase="finalize")

    trace.append(
        {
            "step": 4 if revised else 3,
            "phase": "finalize",
            "final_hash": text_hash(final_answer),
            "ok": True,
        }
    )
    history.append(
        {
            "step": 4 if revised else 3,
            "action": "finalize",
            "status": "final",
        }
    )

    return {
        "run_id": run_id,
        "status": "ok",
        "stop_reason": "success",
        "outcome": "revised_once" if revised else "approved_direct",
        "answer": final_answer,
        "review_decision": review["decision"],
        "issues": review["issues"],
        "fix_plan": review["fix_plan"],
        "trace": trace,
        "history": history,
    }


def main() -> None:
    result = run_reflection_agent(goal=GOAL, incident_context=INCIDENT_CONTEXT)
    print(json.dumps(result, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()

{
  "run_id": "f67eaad4-1c3e-4160-9d8b-1e2e9af42c82",
  "status": "ok",
  "stop_reason": "success",
  "outcome": "revised_once",
  "answer": "We are currently experiencing payment service degradation affecting ~27% of US enterprise checkouts, with failed payments at 3.4% and 5 chargeback alerts. We expect recovery in approximately 45 minutes, but this may change. Next actions: monitor failures every 15 minutes, publish status page updates every 15 minutes, and support users with workaround macros.",
  "review_decision": "revise",
  "issues": [
    {
      "type": "overconfidence",
      "note": "ETA wording sounded too certain and could be read as a guarantee."
    }
  ],
  "fix_plan": [
    "Add uncertainty wording for ETA (for example: 'recovery in approximately 45 minutes, but this may change').",
    "State explicitly that status page updates will be published every 15 minutes.",
    "Keep a clear 'Next actions' section with monitoring, updates, and support macros."
  ],
  "trace": [
    {
      "step": 1,
      "phase": "draft",
      "draft_hash": "f4b3f386c80a",
      "chars": 547,
      "ok": true
    },
    {
      "step": 2,
      "phase": "review",
      "decision": "revise",
      "issues": 1,
      "fix_items": 3,
      "ok": true
    },
    {
      "step": 3,
      "phase": "revise",
      "patch_similarity": 0.564,
      "fix_plan_quoted_checks": 1,
      "revised_hash": "2a5ac4952ae0",
      "ok": true
    },
    {
      "step": 4,
      "phase": "finalize",
      "final_hash": "2a5ac4952ae0",
      "ok": true
    }
  ],
  "history": [{...}]
}