AgentOps Production Readiness Checklist (2026)

Use this checklist to ship an AI agent that is reliable, cost-bounded, and auditable. Treat each line as a release gate.

1) Define the outcome (not the model)
- Write a one-sentence task definition (e.g., “Resolve Tier-1 billing tickets under $200 without human intervention”).
- Choose 2–4 success metrics: task completion rate, cost per successful task, time-to-first-action, policy violations/1,000 runs.
- Set initial targets (example): 70% completion in assisted mode; <0.5 policy violations/1,000; p95 runtime <45s.

2) Constrain the workflow
- List allowed tools explicitly; remove “nice-to-have” tools.
- Define typed tool schemas (JSON) and validate parameters before execution.
- Add hard budgets: max tokens/run, max tool calls, max runtime; specify fallbacks (escalate, safe refusal, ask a clarifying question).

3) Build evaluation before you scale
- Create an eval dataset of 50–200 real scenarios (anonymized) covering common cases + edge cases.
- Define rubrics with pass/fail rules (correctness, tone, compliance, action validity).
- Add regression gates on every prompt/model/tool change; require a changelog entry.

4) Observability and auditability
- Capture traces for every run: inputs, retrieved context IDs, tool calls/params, outputs, policy decisions, and final outcome.
- Add alerts for anomalies: spike in retries, spike in tool failures, drift in completion rate, budget overruns.
- Ensure logs are immutable enough for audits (retention policy + access controls).

5) Security & permissions
- Enforce least privilege: tool scopes by role; never give broad admin tokens to the agent.
- Require approval for irreversible/high-impact actions (e.g., refunds >$200, permission grants, production writes).
- Redact sensitive fields (PII/secrets) at ingestion and before LLM calls where feasible.

6) Rollout plan (stage gates)
- Shadow mode (agent proposes; human executes) with weekly review of failures.
- Assisted execution for low-risk actions; sample-audit at least 5% of runs.
- Supervised autonomy with measurable targets met for 2–4 consecutive weeks before expanding.

7) Cost controls
- Track cost per successful task (not just cost per token).
- Implement routing by difficulty (lightweight model for classification/extraction; stronger model for planning).
- Reduce context bloat via structured summaries and retrieval; cap memory payload size.

8) Human escalation and UX
- Design explicit escalation reasons (“insufficient permissions,” “ambiguous request,” “policy risk”).
- Make outputs structured and editable (drafts, diffs, proposed actions).
- Train operators on how to override, correct, and provide feedback that becomes eval data.

If you can’t prove (with traces + evals) that quality is stable and cost is bounded, you’re not ready to scale.