/well-architected

name: well-architected
description: 6-pillar architecture review framework. Adapted from AWS Well-Architected for use by great_cto's architect agent on every non-nano ARCH document. Forces explicit answers across operational excellence, security, reliability, performance, cost, and sustainability — not just feature design.
when_to_use: |
  Apply when:
  - architect is writing ARCH-*.md for small/medium/large/enterprise project_size
  - regulated-reviewer or security-officer is auditing an existing system
  - project-auditor is reviewing a brownfield codebase
  Do NOT apply to:
  - nano project_size (overhead exceeds value)
  - bug-fix tasks (no architecture change)
  - pure refactor with no behaviour change
effort: high
allowed-tools: Read, Write, Grep, Glob
paths:
  - "docs/architecture/**"
  - "docs/decisions/**"
  - "src/**"

Well-Architected — 6 pillars to verify before shipping

Every ARCH document for non-nano work must answer the 6 pillar questions below. Skipping a pillar is allowed only if explicitly justified (e.g. "Sustainability: N/A — backend-only, runs in shared infra.").

This is adapted from AWS Well-Architected (lens: small-team SaaS / LLM applications), trimmed to questions that matter at <10 engineer scale.

Pillar 1 — Operational excellence

Questions

1. **Observability:** What metrics, logs, traces do we emit? How do we tell from a dashboard if this is working in prod? 2. **Deployability:** How do we ship a change? CI gates? Rollback path? 3. **Runbooks:** When this breaks at 3am, what does on-call read?

Pass criteria

• ✅ One metric per business outcome (e.g. webhook-deliveries-acked)
• ✅ One log line per request, with request-id correlatable across services
• ✅ Deploy path is documented and tested (rollback dry-run executed)
• ✅ Runbook covers top-3 failure modes from pre-mortem

Common fail

❌ "We'll add monitoring later." Monitoring is part of the feature.

Pillar 2 — Security

Questions

1. **Trust boundaries:** Where does untrusted data enter? How is it validated/sanitized? 2. **Authn / authz:** Who can call this? Who can read/write the data? 3. **Secrets:** Where are API keys, DB passwords, JWT signing keys stored? 4. **Data classification:** PII? PHI? PCI cardholder data? What's the retention policy?

Pass criteria

• ✅ Every external input has explicit validation at the boundary
• ✅ Authz is enforced at the data layer, not just UI
• ✅ Secrets in env vars or secret manager, never in source
• ✅ Sensitive data classified and retention policy defined

Common fail

❌ "JWT validates the user, that's our authz." JWT is authentication. Authorization is separate (this user can read THIS row).

Pillar 3 — Reliability

Questions

1. **Failure modes:** What happens when a downstream dependency is slow / down / corrupted? 2. **Idempotency:** Can a retried request safely re-execute? 3. **Backups & recovery:** What's the RPO (data-loss tolerance)? RTO (downtime tolerance)? Test plan for both? 4. **Capacity:** What's the max QPS this can handle? What happens at 1.5x that?

Pass criteria

• ✅ Circuit breakers / timeouts on external calls
• ✅ State-mutating endpoints accept idempotency keys
• ✅ Backups documented + restore tested in the last 90 days
• ✅ Load test exists; results in `docs/perf/`

Common fail

❌ "Postgres has backups." Backups without a tested restore aren't backups.

Pillar 4 — Performance efficiency

Questions

1. **SLOs:** What's the p50/p95/p99 latency target? Error rate? Availability? 2. **Bottlenecks:** Profile the critical path — what's the slowest step? 3. **Caching:** What's cacheable? Cache invalidation strategy? 4. **Scaling:** Vertical or horizontal? Auto-scale rules?

Pass criteria

• ✅ SLO numbers in the ARCH doc (not "fast enough")
• ✅ Profile attached for non-trivial requests
• ✅ Cache strategy documented; invalidation explicit
• ✅ Scaling decision justified by data, not "feels right"

Common fail

❌ "Database can handle it." Quantify: queries/sec, row count, index hit rate.

Pillar 5 — Cost optimization

Questions

1. **Hot path:** What's the most expensive operation per request? Why? 2. **Right-sizing:** Is the chosen instance type / model / DB tier the smallest one that meets SLO? 3. **Cleanup:** What happens to old data? Old logs? Old branch environments?

Pass criteria

• ✅ Use skill `cost-model` to document explicit $ numbers
• ✅ Choose smallest LLM model that meets quality SLO (haiku before sonnet,

sonnet before opus)

• ✅ Retention policy for logs, metrics, old data

Common fail

❌ Defaulting to Opus / GPT-4 when Haiku would work. Test on Haiku first.

Pillar 6 — Sustainability (env / energy)

Questions

1. **Workload efficiency:** Is the code O(n log n) when it could be O(n)? 2. **Idle resources:** Can dev environments scale to zero overnight? 3. **Data minimization:** Do we collect / store data we never query?

Pass criteria

• ✅ Hot loop complexity documented
• ✅ Non-prod resources have shutdown schedules
• ✅ Data lifecycle covers ingestion, retention, deletion

Common fail

❌ Logs at debug level in prod, never reviewed. Waste of storage + carbon.

Output format — add to ARCH

## Well-Architected review

### 1. Operational excellence
- Metrics: <list>
- Deploy path: <link to runbook>
- Verdict: PASS | RISKS LISTED

### 2. Security
- Trust boundaries: <list>
- Data classification: <PII / PHI / PCI / none>
- Verdict: PASS | RISKS LISTED

### 3. Reliability
- Failure modes: <link to pre-mortem>
- Idempotency: <yes/no per endpoint>
- Verdict: PASS | RISKS LISTED

### 4. Performance
- SLOs: p99=<ms>, error_rate=<%>, availability=<%>
- Verdict: PASS | RISKS LISTED

### 5. Cost
- Per-request cost: $<amount>
- Verdict: PASS | RISKS LISTED

### 6. Sustainability
- Hot-path complexity: O(<n>)
- Verdict: PASS | N/A | RISKS LISTED

## Open risks (rolled up)

<bullet list of all RISKS LISTED items + mitigati