# Code Review — AI Agent Requirement Closure

**Date:** 2026-02-24  
**Scope:** Ghostfolio finance agent requirement closure (`docs/requirements.md`)  
**Status:** ✅ Core technical requirements complete (local verification gate passed, including strict live-latency check)

## Summary

The previously open requirement gaps are closed in code and tests:

1. Eval framework expanded to 50+ deterministic cases with category minimum checks.
2. LangSmith observability integrated for chat traces and eval-suite tracing.
3. User feedback capture implemented end-to-end (API + persistence + UI actions).
4. Local verification gate completed without pushing to `main`.
5. Reply quality guardrail and eval slice added.
6. Live model/network latency gate added and passing strict targets.

## What Changed

### 1) Eval Dataset Expansion (50+)

- Dataset now exports **53 cases**:
  - `happy_path`: 23
  - `edge_case`: 10
  - `adversarial`: 10
  - `multi_step`: 10
- Category assertions are enforced in `mvp-eval.runner.spec.ts`.
- Dataset organization uses category files under:
  - `apps/api/src/app/endpoints/ai/evals/dataset/`

### 2) Observability Integration

- Chat observability in API:
  - `apps/api/src/app/endpoints/ai/ai-observability.service.ts`
  - `apps/api/src/app/endpoints/ai/ai.service.ts`
- Captures:
  - latency (total + breakdown)
  - token estimates
  - tool trace metadata
  - failure traces
- LangSmith wiring is environment-gated and supports `LANGSMITH_*` and `LANGCHAIN_*` variables.

### 3) Feedback Loop (Thumbs Up/Down)

- API DTO + endpoint:
  - `apps/api/src/app/endpoints/ai/ai-chat-feedback.dto.ts`
  - `POST /api/v1/ai/chat/feedback`
- Persistence + telemetry:
  - feedback saved in Redis with TTL
  - feedback event traced/logged through observability service
- UI action wiring:
  - `apps/client/src/app/pages/portfolio/analysis/ai-chat-panel/`
  - user can mark assistant responses as `Helpful` or `Needs work`

### 4) Reply Quality Guardrail

- Quality heuristics added:
  - anti-disclaimer filtering
  - actionability checks for invest/rebalance intent
  - numeric evidence checks for quantitative prompts
- New verification check in responses:
  - `response_quality`
- New quality eval suite:
  - `apps/api/src/app/endpoints/ai/evals/ai-quality-eval.spec.ts`

### 5) Live Latency Gate

- New benchmark suite:
  - `apps/api/src/app/endpoints/ai/evals/ai-live-latency.spec.ts`
- Commands:
  - `npm run test:ai:live-latency`
  - `npm run test:ai:live-latency:strict`
- Latest strict run:
  - single-tool p95: `3514ms` (< `5000ms`)
  - multi-step p95: `3505ms` (< `15000ms`)
- Tail-latency guardrail:
  - `AI_AGENT_LLM_TIMEOUT_IN_MS` (default `3500`) with deterministic fallback.

### 6) Eval Quality Metrics (Tracked)

- `hallucinationRate` added to eval suite result with threshold gate `<= 0.05`.
- `verificationAccuracy` added to eval suite result with threshold gate `>= 0.9`.
- Both metrics are asserted in `mvp-eval.runner.spec.ts`.

## Verification Results

Commands run locally:

```bash
npm run test:ai
npm run test:mvp-eval
npm run test:ai:quality
npm run test:ai:performance
npm run test:ai:live-latency:strict
npx nx run api:lint
npx dotenv-cli -e .env.example -- npx jest apps/client/src/app/pages/portfolio/analysis/ai-chat-panel/ai-chat-panel.component.spec.ts --config apps/client/jest.config.ts
```

Results:

- `test:ai`: passed (9 suites, 40 tests)
- `test:mvp-eval`: passed (category gate + pass-rate gate)
- `test:ai:quality`: passed (reply-quality eval slice)
- `test:ai:performance`: passed (service-level p95 gate)
- `test:ai:live-latency:strict`: passed (real model/network p95 gate)
- `api:lint`: passed (existing workspace warnings remain non-blocking)
- client chat panel spec: passed (4 tests, including feedback flow)

## Requirement Mapping (Technical Scope)

| Requirement | Status | Evidence |
| --- | --- | --- |
| 5+ required tools | ✅ | `determineToolPlan()` + 5 tool executors in AI endpoint |
| 50+ eval cases + category mix | ✅ | `mvp-eval.dataset.ts` + `evals/dataset/*` + category assertions in spec |
| Observability (trace, latency, token) | ✅ | `ai-observability.service.ts`, `ai.service.ts`, `mvp-eval.runner.ts` |
| User feedback mechanism | ✅ | `/ai/chat/feedback`, Redis write, UI buttons |
| Verification/guardrails in output | ✅ | verification checks + confidence + citations + `response_quality` in response contract |
| Strict latency targets (`<5s` / `<15s`) | ✅ | `test:ai:live-latency:strict` evidence in this review |
| Hallucination-rate tracking (`<5%`) | ✅ | `mvp-eval.runner.ts` metric + `mvp-eval.runner.spec.ts` threshold assertion |
| Verification-accuracy tracking (`>90%`) | ✅ | `mvp-eval.runner.ts` metric + `mvp-eval.runner.spec.ts` threshold assertion |

## Remaining Non-Code Submission Items

These are still manual deliverables outside local code/test closure:

- Demo video (3-5 min)
- Social post (X/LinkedIn)
- Final PDF packaging of submission docs