LLM Control Plane Starter Checklist (2026)

Use this to audit your current LLM/agent setup or to design the first version of a centralized AI gateway.

1) Centralize model access
- Do all model calls go through one internal endpoint (gateway/proxy)?
- If not, identify every service calling a model API directly and plan a migration path.

2) Define routing rules
- List your workflows (e.g., “support answer,” “code review,” “contract extraction”).
- For each workflow, define: latency budget, acceptable refusal behavior, and fallback model.

3) Enforce structured outputs
- For workflows that feed downstream code, require JSON Schema (or equivalent) output.
- Define “fail closed” behavior: what happens if schema validation fails (retry, fallback, or human review).

4) Tool permissions are deny-by-default
- Maintain an allowlist of tools per workflow.
- Scope tool credentials (OAuth scopes / least privilege). Avoid shared “god tokens.”

5) Circuit breakers for agent loops
- Set max tool calls per request.
- Set max total runtime per request.
- Set max retries for model calls and tool calls.

6) Trace everything end-to-end
- Propagate a trace_id across retrieval, model calls, tool calls, and post-processing.
- Record model name/version, prompt template version, and tool call arguments/results.

7) Logging policy that won’t create a privacy incident
- Decide what you log, where it lives, and how long you keep it.
- Redact PII before logs. Separate audit logs from prompt/content logs.

8) Build workflow-level golden sets
- Create a small curated set of real examples per workflow.
- Store expected outputs or acceptance criteria (schema validity, citations present, policy compliance).

9) Put evals in CI
- Any change to prompts, retrieval configuration, tool definitions, or routing should run evals.
- Define a release gate: what failures block deployment.

10) Online monitors for drift
- Alert on spikes in: schema failures, tool-call counts, refusal rates, latency, and fallback usage.
- Review a small sampled set of traces weekly with engineering + product.

11) Human approval for high-risk actions
- Identify actions that write data, send messages, charge money, or change permissions.
- Require explicit user confirmation or a review step before execution.

12) Incident playbooks
- Write down how to: disable a tool, force a safe model route, roll back a prompt version, and purge logs if needed.
- Make sure on-call can do these actions without a code deploy.

If you can’t point to where each item lives in your system (a file, a service, a dashboard, a policy), treat it as missing.