AI Model Router Readiness Checklist (2026)

Use this to assess whether your AI feature is a prototype glued to a single provider or a production system you can operate.

1) Define the AI contract (write it down)
- Inputs: what data classes can enter the model (PII, secrets, customer content)?
- Outputs: exact JSON schema or response format; tool-call schema if applicable.
- Policy: refusal expectations for risky categories; what must never be answered.
- Latency budget: separate interactive vs background tasks.

2) Build a provider-neutral interface
- One internal API for “generate”, “classify”, “extract”, “tool_call”.
- Provider adapters live behind the interface (OpenAI, Anthropic, Google, Azure OpenAI, self-hosted).
- Prompt templates are versioned artifacts (not scattered strings across the codebase).

3) Add routing rules before fine-tuning
- Route by task type (extraction vs drafting vs ranking).
- Route by risk (legal/medical/security-sensitive queries).
- Route by data constraints (PII → approved environment; region restrictions).
- Route by tier (enterprise vs free) and by incident state (degrade gracefully).

4) Enforce validation gates
- Schema validation for structured outputs.
- Business-rule validation for tool arguments (allowlists, bounds, forbidden actions).
- Retry policy: deterministic retries with caps; escalation to a stronger model if validation fails.

5) Evals you can run every release
- Golden set from real traffic (redacted) for each critical workflow.
- Regression tests for prompt versions and retrieval changes.
- Red-team set for prompt injection and policy bypass attempts.
- A documented pass/fail threshold per workflow (qualitative is fine if consistent).

6) Observability that supports on-call
- Trace ID per request; log model, version, prompt version, and routing decision.
- Log retrieval provenance (document IDs/URLs) and tool calls (names + validated args).
- Redaction rules so logs don’t become a data leak.
- Dashboards for error rates, validation failures, refusals, and latency tails.

7) Degradation and failover
- Timeouts that return safe partial results rather than hanging UX.
- Fallback model(s) per workflow.
- Cached safe responses for common requests when providers are degraded.
- Circuit breaker rules: when to stop sending traffic to a failing provider.

8) Run a “forced migration” drill
- Route a small slice (e.g., canary) to an alternate model/provider behind the same contract.
- Verify outputs pass schema + quality gates.
- Confirm product code doesn’t change; only routing/config changes.
- Document what broke and convert it into contract tests.

If you can’t complete the forced migration drill quickly, your next sprint isn’t ‘better prompts’. It’s building the router, the eval harness, and the contracts that make models replaceable.