Evaluation & Operations
Flagship field guide
MDX
Production Readiness Checklist for AI Agents
A practical sequence for validating scope, quality, security, operations, and rollback before launch.
Editorial review: clarity, operational relevance, safety boundaries, and source quality.
Define scope and success
Document supported tasks, excluded tasks, permissions, stop conditions, and measurable success criteria. Confirm that a simpler workflow would not deliver the same value more reliably.
A production contract names supported users and tasks, excluded cases, required evidence, permissions, budgets, terminal states, and service expectations. Document the deterministic baseline and why agentic choice improves it. Assign an owner for product quality and another clear owner for incident response.
Test quality and abuse
Run representative evaluations, edge cases, prompt-injection attempts, tool failures, permission checks, and load tests. Review both final outcomes and full action trajectories.
Testing should cover real distributions, edge cases, adversarial content, permission boundaries, tool degradation, uncertain writes, concurrency, and cancellation. Evaluate final state and trajectory. Run shadow or draft-only traffic before granting writes, then canary the smallest capability with automatic rollback thresholds.
Prepare operations
Set budgets, alerts, trace access, incident ownership, kill switches, and a rollback path. Launch gradually and preserve a reliable human route while evidence accumulates.
Operational readiness includes versioned prompts and tools, trace access, redaction, spend and loop alerts, queues for human handoff, credential revocation, a kill switch, and a tested rollback. Dashboards should show customer outcomes and policy violations alongside tokens and latency.
Practical example
A staged production launch
A ticket agent first runs in shadow mode, where its routes are compared with human decisions. It then suggests routes in the UI, followed by autonomous routing for two low-risk categories. Write access is capability-scoped, error and override thresholds trigger rollback, and every ticket retains a human path. Broader categories wait until scenario-level evidence meets the launch gate.
Field checklist
Apply it in practice
- Publish the task contract, risk tier, and named owners.
- Test abuse, degraded tools, uncertain writes, and cancellation.
- Launch through shadow, assistive, and narrow canary stages.
- Exercise kill switch, rollback, handoff, and incident procedures.
Decision framework
Questions to answer before you build
Production readiness is evidence that a bounded task works under realistic, degraded, and adversarial conditions—and that operators can observe, stop, recover, and roll it back.
Is the task contract publishable?
Document supported users, inputs, outcomes, exclusions, permissions, evidence, budgets, terminal states, and named owners.
Has failure been exercised?
Test tool outages, permission denials, uncertain writes, prompt injection, concurrency, cancellation, stale data, and handoff quality.
Can launch be contained?
Begin in shadow or assistive mode, canary one low-risk capability, define rollback thresholds, and preserve a reliable human route.
Common failure signals
Watch for these warning signs
- Launching writes after successful demos but before trajectory evaluation.
- Having dashboards without a tested kill switch or incident owner.
- Changing models or tools without rerunning the risk-relevant evaluation slices.
Field manual
Implementation blueprint
- 01
Review the task contract
Confirm supported users, outcomes, exclusions, evidence, permissions, budgets, and terminal states match the deployed configuration.
Deliverable: A versioned contract signed off by product and operational owners.
- 02
Inspect evaluation evidence
Review representative and adversarial results by risk slice, including blocked unsafe attempts and human corrections.
Deliverable: A release report with known limitations and unresolved failure classes.
- 03
Exercise operations
Test pause, cancel, retry, reconciliation, escalation, rollback, incident response, and degraded dependencies.
Deliverable: Evidence that failure can be contained and recovered, not merely detected.
- 04
Limit the launch
Choose shadow, assistive, supervised, or bounded mode per capability and define expansion and rollback triggers.
Deliverable: A staged release plan tied to observed outcomes.
Reusable working artifact
Production release gate
Use this as a decision record. A red control is not balanced out by high average answer quality.
CAPABILITY: [one bounded job or action]
RELEASE MODE: shadow | assistive | supervised | bounded
GATE EVIDENCE OWNER STATUS
Task contract version + approved scope Product G/Y/R
Representative evaluation results by task/risk slice Quality G/Y/R
Security trajectory tests attacks, attempts, effects Security G/Y/R
Permission review effective capability matrix Platform G/Y/R
Operational rehearsal stop/recover/rollback log Operations G/Y/R
Privacy review data map + retention tests Privacy G/Y/R
Human handoff sampled handoff quality Service G/Y/R
NO-GO CONDITIONS
[prohibited effect, missing owner, unreconciled write, untested rollback]
CANARY LIMITS
[users, volume, data, actions, budget, duration]
ROLLBACK TRIGGERS
[metric + threshold + window + accountable decision maker]Measurement
Operational scorecard
Failure drills
Rehearse before the system has real authority
- Disable a critical tool during a canary and verify stop, user communication, and recovery behavior.
- Simulate an indeterminate write and require authoritative reconciliation before continuing.
- Roll back a model or prompt change using the documented trigger and measure time to safe state.
Selected primary references
Continue with the source material
These sources inform the wider editorial perspective for this topic. They are not presented as line-by-line citations for every statement.
- NISTAI Risk Management Framework ↗A voluntary framework for governing, mapping, measuring, and managing AI risk.
- OWASPOWASP Top 10 for LLM Applications 2025 ↗A practical catalogue of common security risks in LLM-enabled applications.
- OWASPLLM06:2025 Excessive Agency ↗How excessive functionality, permissions, and autonomy create damaging agent behavior.
- AnthropicDemystifying evals for AI agents ↗Evaluation strategies for multi-step, tool-using agent systems.