AgentOps Production Readiness Checklist (2026)

Use this checklist to move from “cool demo” to “reliable system.” Treat it like a go/no-go gate for expanding autonomy.

1) Define the workflow and success criteria
- Specify a single workflow (e.g., ticket triage, refund eligibility, PR review).
- Write a definition of “task success” that is testable (pass/fail), not subjective.
- Set an escalation rule (when should the agent stop and hand off?).
- Set SLOs: latency budget (interactive vs background) and uptime expectations.

2) Build an evaluation suite before scaling
- Collect 200–1,000 real historical examples (de-identified if needed).
- Label outcomes: correct action, correct policy, correct tool usage.
- Add automated checks: JSON/schema validity, citation required, PII leakage check, policy violations.
- Establish release gates: do not ship if success rate drops >2% or unsafe-write attempts rise.

3) Instrumentation and observability (non-negotiable)
- Trace every run: inputs, retrieved context IDs, model calls, tool calls, outputs.
- Log every write with an immutable audit trail (who/when/what changed).
- Add failure taxonomy: tool error, missing context, ambiguity, model refusal, policy block.
- Create dashboards per workflow: success rate, escalation rate, cost per successful task, latency.

4) Security and permissions
- Implement least-privilege tool scopes (no generic “HTTP request” in production).
- Separate read vs write credentials; rotate secrets on a schedule.
- Enforce tool-call policies outside the model (middleware/policy engine).
- Treat external text as untrusted; restrict actions derived from untrusted inputs.
- Require approvals for: money movement, identity/account changes, production changes, external customer comms.

5) Cost controls and unit economics
- Define a cost-per-successful-task budget (e.g., $0.10–$0.25 high-volume; $0.50–$2.00 complex).
- Add loop/retry caps: max 3 retries; stop on repeated tool errors.
- Use model routing: small model for classify/extract, large model only when needed.
- Reduce tokens: summarize context, avoid stuffing full documents, cache retrieval.
- Set alerts: notify if weekly cost/task rises >25% or token usage spikes.

6) Rollout plan (progressive autonomy)
- Phase 1: read-only recommendations.
- Phase 2: drafts/internal notes (low-risk writes).
- Phase 3: constrained writes with approvals.
- Phase 4: limited autonomous writes within strict scopes.
- Expand scope only after meeting targets for 2–4 consecutive weeks.

7) Operational process
- Establish an owner for the agent workflow (like a product owner).
- Create an incident playbook: rollback, disable writes, switch to fallback mode.
- Run weekly review: top failures, new edge cases, dataset updates, policy changes.
- Maintain versioning for prompts, tool schemas, and policies; track what changed.

If you can’t check these boxes, keep the agent in “assist mode.” Reliability beats autonomy—and the fastest way to earn trust is measurable performance with tight controls.