# AWAF Assessment — File Summarizer Agent

**AWAF v1.0 | 2026-03-15**

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## Overall Score: 89 — Near Ready

> **Why 89 is a strong result.** The AWAF scale is calibrated against operational reality, not code quality. Most production AI agents score in the **30–55 range** — they ship with clean code but no runbooks, no kill switch, no evals, and secrets in environment variables. The scale reflects that:
>
> | Score | Rating | What it means in practice |
> |-------|--------|--------------------------|
> | 0–24 | Not Ready | Do not ship — structural problems that will cause outages |
> | 25–49 | High Risk | Will cause production incidents; architectural rework needed |
> | 50–74 | Needs Work | Meaningful gaps; addressable but not quickly |
> | **75–89** | **Near Ready** | **Minor gaps; addressable before go-live** |
> | 90–100 | Production Ready | Sound patterns across all 10 pillars |
>
> At 89, every pillar returned **verified** confidence — meaning the assessor found real evidence (code, configs, docs, evals) for every claim, not self-reporting. That is uncommon. Most agents receive `partial` or `self_reported` on the Tier 2 pillars (Reasoning Integrity, Controllability, Context Integrity) because those require working evals, signal-based kill switches, and active context management — easy to defer, hard to retrofit.
>
> The two remaining High findings (circuit breaker, checkpoint/resume) are **known and documented** in the postmortem with assigned owners. A gap that is measured and tracked is meaningfully less risky than one that is invisible.

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| Tier | Pillar | Score | Confidence |
|------|--------|-------|------------|
| **Tier 0** | Foundation | 92 | verified ✓ PASS |
| **Tier 1** | Operational Excellence | 92 | verified |
| **Tier 1** | Security | 88 | verified |
| **Tier 1** | Reliability | 88 | verified |
| **Tier 1** | Performance Efficiency | 92 | verified |
| **Tier 1** | Cost Optimization | 95 | verified |
| **Tier 1** | Sustainability | 85 | verified |
| **Tier 2 (1.5×)** | Reasoning Integrity | 82 | verified |
| **Tier 2 (1.5×)** | Controllability | 95 | verified |
| **Tier 2 (1.5×)** | Context Integrity | 85 | verified |

Files analyzed: 11

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## Key Remaining Findings

### High Severity
- **Reliability:** No circuit breaker at session level — each file retries independently against a degraded API (documented in postmortem but not yet fixed)
- **Reliability:** No checkpoint/resume for batch runs — a mid-batch failure requires full restart

### Medium Severity (selected)
- **Op. Excellence:** CloudWatch alarms defined but no `boto3` metric emission in code
- **Op. Excellence:** Postmortem action items (circuit breaker, checkpoint) not yet implemented
- **Security:** API key from env var — no secrets manager integration for production rotation
- **Reasoning:** Golden dataset has only 3 cases — insufficient for statistically meaningful hallucination rate
- **Reasoning:** Prompt snapshot truncated to 500 chars — full reasoning trace not auditable

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## Top Recommendations to Reach Production Ready (>= 90)

1. Implement a session-level circuit breaker in `agent.py` (trips after N consecutive failures across files)
2. Add checkpoint file persistence so batch runs can resume after failure
3. Emit CloudWatch metrics via `boto3.put_metric_data()` to activate the defined alarms
4. Expand `evals/golden_dataset.json` to 50+ cases for meaningful hallucination measurement
5. Add `SIGUSR1` handler for runtime `SESSION_TOKEN_LIMIT` adjustment without restart
