eaf282c535
- TD#8 hybrid.ts: rerank_strategy {always|when_top_k_gt|never} + threshold
(default skips rerank for top_k ≤ 15; chat tool uses threshold 10)
- O11 vision.ts + tools.ts: analyze_image_region tool — sharp-crops the
bbox, claude CLI reads the temp PNG via Read tool, Sonnet vision answers
- TD#12 /graph: SigmaGraph replaces ForceGraphCanvas; react-force-graph-2d
uninstalled (-37 transitive deps); force-graph-canvas.tsx deleted
- TD#27 messages/route.ts gatherContext slice sizes via CTX_* env vars
- TD#22 tests/rag/: golden.yaml (15 queries) + run.py (Recall@k + MRR +
negative-pass rate) + baseline.json + CI job in .forgejo/workflows/ci.yml
- docs/adrs/: ADR-001..005 published from systems-atelier deliverables
Verified live on disclosure.top: top_k=5 path skips rerank (6.7s embed-only,
was 12-15s with rerank); rerank=always still available on demand.
First RAG baseline: Recall@5 = 0.2083, MRR = 0.25, Negative pass = 1.0.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
77 lines
4 KiB
Markdown
77 lines
4 KiB
Markdown
---
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adr: ADR-002
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title: Materializar Investigation Bureau — runtime agentico em background, 8 detetives como roles
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status: accepted
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date: 2026-05-23
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deciders: sa-principal, sa-architecture-lead, sa-security-engineer (veto power)
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project: disclosure-bureau
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---
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## Context
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O branding "The Disclosure Bureau" promete "8 detetives investigativos" (Holmes/Poirot/Dupin/Locard/Schneier/Tetlock/Taleb + Investigation Bureau coletivo) com chain of custody, hypothesis tournament, residual uncertainty calculation. Hoje, o codebase tem:
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- `case/` filesystem com 6 pastas — 5 vazias, 1 com 2 gap files.
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- Chat com 12 tools read-only e um system prompt grandioso.
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- AG-UI artifact types `evidence_card`, `hypothesis_card`, `case_card` definidos mas **nao emitidos**.
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- Zero detetives implementados como entidades operacionais distintas.
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O brief pede: "AI detective bureau REAL, nao decorativo". Isso requer **producao** de dado novo (`case/evidence/*.md`, `case/hypotheses/*.md`, `public.{hypotheses,evidence,contradictions,...}`) por **agentes especializados** com **outputs estruturados e auditaveis**.
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Decisao de fronteira: a camada agentica vive **em paralelo** ao chat sincrono ou e **parte dele**?
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## Options considered
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1. **Parte do chat sincrono.** Estender system prompt + adicionar write tools. Usuario espera 30s-5min sincrono.
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2. **Worker em background.** Chat dispara job; usuario polls; worker assincrono produz outputs.
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3. **Sem agentic layer**: manter so chat read-only. Refatorar branding para refletir realidade ("AI-assisted wiki").
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4. **CronJob batch only**. Sem trigger user. Investigacoes acontecem em background diario.
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## Decision
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**Opcao 2: Worker em background, separado do chat sincrono.**
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Especificamente:
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1. **Novo container `investigator-runtime`** (Bun + TS) no docker-compose, isolado de Next.js.
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2. **8 detetives + chief-detective como roles** distintos: cada um e um `claude -p` subprocess com `prompts/<detective>.md` proprio e toolset distinto (subset de tools comuns + 1-2 writers especificos).
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3. **Postgres LISTEN/NOTIFY** como queue (`public.investigation_jobs` + trigger NOTIFY).
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4. **Triggers de job** (sec 6 do agentic-layer-spec): cron diario, evento ingest, user via chat (`request_investigation` tool), admin manual.
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5. **Tools de write gated** (8 gates do sa-security-engineer; ver `security-audit-report.md` secao 5).
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6. **Budget cap por job:** $1.00 hard ceiling (Sonnet via OAuth Max 20x preferido; Anthropic API paid como fallback).
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7. **Outputs validados antes de commit:** schema check + lint (`04-lint.py --dry-run`) sobre markdown gerado.
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**Nao adotamos:**
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- Opcao 1 (estender chat sincrono): user nao pode esperar 5 min num chat. Quebra modelo mental.
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- Opcao 3 (sem agentic): foge do brief explicito. Branding sem motor e desonesto.
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- Opcao 4 (cron only): sem trigger user e UX pobre. Manter cron como complementar, nao exclusivo.
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## Consequences
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**Positivas:**
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- Branding "8 detetives" passa a ter motor real.
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- Chat sincrono continua rapido (LLM read-only + 12 tools).
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- Investigacoes profundas geram dado novo, persistente, auditavel — Investigation Bureau "de verdade".
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- Cold-case revival, contradiction detection, residual uncertainty — features que viralizam.
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**Negativas:**
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- Novo container = nova superficie operacional (~150MB RAM extra; orchestrator + state).
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- Quota Claude Max 20x mais utilizada (ja monitorada por `/api/admin/batch`).
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- Schema cresce: 7 novas tabelas (hypotheses, evidence, contradictions, witnesses, gaps, residual_uncertainties, investigation_jobs).
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- Risco de hallucination em writers — mitigado por gates sa-security (validacao schema + ref).
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## Verification
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- Spec completa em `agentic-layer-spec.md`.
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- Plano de bring-up incremental em 10 sub-steps W3.1-W3.10.
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- 8 gates documentados para sa-security veto.
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- Custos esperados $30-110/mes (tabela secao 11 do spec).
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- Golden hypothesis set como quality bar (W3.10).
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## References
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- `agentic-layer-spec.md`
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- `ai-opportunity-map.md` O1-O5
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- `security-audit-report.md` secao 5
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- Anthropic Claude Code OAuth pattern (memoria do projeto)
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