AI Observability Readiness Checklist (2026 Edition)

Goal: In 10–14 days, reach a baseline where you can (1) reconstruct what an agent did, (2) detect quality regressions, (3) attribute cost to tenants/workflows, and (4) prove key policy checks ran on critical paths.

1) Identify critical paths (Day 1)
- List the top 3 AI workflows tied to revenue, risk, or customer trust (examples: support drafting, invoice triage, account research).
- For each workflow, define 2–4 acceptance criteria (examples: citations present where required; no restricted sources; tool actions match user intent; p95 response time meets your product target).
- Assign a single accountable owner per workflow (product or engineering).

2) Instrument end-to-end tracing (Days 2–5)
- Pick a canonical trace ID and propagate it through gateway → orchestrator → retrieval → tools → model calls.
- Add structured span attributes: tenant_id, user_role, model, prompt_hash, tool_name, index_version, input/output tokens, cost_usd.
- Record retrieval evidence as doc IDs and similarity scores (avoid storing raw content by default).
- Add redaction before storage: remove or mask PII and secrets from prompts and tool outputs.

3) Build a replay set (Days 4–7)
- Sample 200–1,000 real traces that represent normal usage (sanitized and permissioned).
- Store minimal replay bundles: user input, resolved prompt template version, doc IDs or an index snapshot reference, and tool outputs.
- Tag bundles by tenant segment, language, and workflow type so slices are easy to compare.

4) Stand up evaluations (Days 6–10)
- Offline eval: run the replay set on a schedule; track acceptance criteria pass rates.
- Online monitoring: score a small sample of production traffic for faithfulness, tool success, and policy compliance.
- Define regression thresholds that trigger investigation or rollback (examples: faithfulness drops; tool failures rise; cost per successful task rises).

5) Add cost controls (Days 8–12)
- Attribute cost per tenant and per workflow (not just total tokens).
- Alert on anomalies: daily spend spikes, output length spikes, tool calls per session spikes, unusual retry patterns.
- Add caching where it is safe: prompt/result caching; retrieval caching for stable documents.

6) Governance and audit readiness (Days 10–14)
- Record policy checks as structured events (PII handling, tenant isolation, restricted-source filtering).
- Set retention rules: short retention for raw text; longer retention for derived features.
- Apply role-based access to observability data and treat it as sensitive production data.

Exit criteria (baseline ready)
- You can pull a trace for a specific user request quickly.
- You can answer: what sources were used, what tools were called, what it cost, and which policy checks ran.
- You have a repeatable regression report and a canary/rollback plan for prompt, model, and routing changes.