01
Job and ownership
Define what the agent owns, what success means and who owns exceptions.
Free AI agent readiness assessment
AI Agent Production Readiness Checklist
An AI agent production readiness checklist is a structured review of the job, evaluations, permissions, failure handling, observability and human controls required before an agent performs live business work. This free scorecard turns 24 production gates into a documented go, conditional pilot or hold decision.
What this evaluates
Six dimensions separate a demo from an operated agent
The scorecard combines current evaluation, security and risk guidance into one operational review. It does not replace a threat model, legal review or live validation.
02
Evaluation
Test realistic behavior before exposing the agent to live work.
03
Permissions and data
Limit knowledge, credentials, tools and actions to the minimum required.
04
Reliability and recovery
Design predictable failure handling, retries and a safe way back.
05
Observability and cost
Make decisions, tool calls, quality, latency and cost visible.
06
Human control and incidents
Keep people in control of sensitive actions, escalation and response.
How the decision works
Critical gates come before the percentage
24 observable gates
Each item asks for evidence such as a test set, permission inventory, trace, runbook or approval rule.
Eight critical blockers
Unknown ownership, evaluation, permissions, rollback, tracing, approval or incident response prevents a pilot-ready result.
Portable Markdown
Copy or download the current review without creating an account or sharing company data.
Interactive scorecard
Review all 24 production gates
Mark each gate as ready or a gap. Unreviewed critical gates block the result because unknown risk is still risk.
01
Job and ownership
Define what the agent owns, what success means and who owns exceptions.
The agent has one named business job and a documented boundary.
Critical gateEvidence to keep: Job statement, included tasks and excluded tasks.
A named person owns outcomes, exceptions and expansion decisions.
Critical gateEvidence to keep: Owner, backup owner and escalation path.
Success is measured against a current human or software baseline.
Evidence to keep: Baseline, target metric and review window.
Known exceptions and out-of-scope requests have defined handling.
Evidence to keep: Exception catalog and handoff rules.
02
Evaluation
Test realistic behavior before exposing the agent to live work.
The evaluation set represents real tasks, edge cases and failures.
Critical gateEvidence to keep: Versioned test cases sampled from the target workflow.
Each evaluation has observable acceptance criteria.
Evidence to keep: Expected result, allowed variation and failure definition.
Tool selection, arguments and final outcomes are evaluated separately.
Evidence to keep: Tool-call assertions and end-to-end outcome checks.
Prompt, model and tool changes run through regression evaluation.
Evidence to keep: Release gate with retained comparison results.
03
Permissions and data
Limit knowledge, credentials, tools and actions to the minimum required.
Every tool and credential follows least privilege.
Critical gateEvidence to keep: Scoped service accounts, permissions and credential inventory.
Allowed data sources, retention and sensitive-data rules are explicit.
Evidence to keep: Data inventory, classification and retention policy.
Untrusted input is isolated from instructions, secrets and privileged actions.
Evidence to keep: Input boundaries, sanitization and prompt-injection tests.
Agent output is validated before it reaches another system.
Evidence to keep: Schemas, allowlists and business-rule validation.
04
Reliability and recovery
Design predictable failure handling, retries and a safe way back.
Timeouts, retries and duplicate prevention are defined per tool.
Evidence to keep: Retry policy, idempotency controls and timeout budget.
The agent fails safely when a model, API or knowledge source is unavailable.
Evidence to keep: Fallback behavior and dependency failure tests.
A tested kill switch and rollback path can stop harmful behavior.
Critical gateEvidence to keep: Runbook, responsible operator and last drill result.
Concurrency, rate and resource limits are enforced.
Evidence to keep: Configured limits and load-test results.
05
Observability and cost
Make decisions, tool calls, quality, latency and cost visible.
Each run records decisions, tool calls, outcomes and errors.
Critical gateEvidence to keep: Searchable traces with access and retention controls.
Live quality and failure signals have thresholds and owners.
Evidence to keep: Dashboard, alert thresholds and response owner.
Cost and latency are measured per completed business unit.
Evidence to keep: Unit-cost dashboard and budget alerts.
Logs are useful for audit without exposing secrets or unnecessary personal data.
Evidence to keep: Redaction tests, access policy and audit sample.
06
Human control and incidents
Keep people in control of sensitive actions, escalation and response.
Sensitive, external or irreversible actions require human approval.
Critical gateEvidence to keep: Action risk matrix and enforced approval gates.
The agent can transfer context and work to a person without losing state.
Evidence to keep: Handoff test with context, reason and next action.
Agent incidents have a response path, owner and communication rule.
Critical gateEvidence to keep: Incident runbook, severity levels and contact path.
A recurring review decides whether to keep, change, expand or stop the agent.
Evidence to keep: Review calendar, decision log and retirement criteria.
Use the result
Treat readiness as an operating decision, not a badge
Hold
A critical gate is open or fewer than 65% of all gates are ready. Keep the agent out of expanded live work.
Conditional pilot
Every critical gate is ready and the total score is 65% to 84%. Run a bounded pilot with close supervision.
Pilot-ready
Every critical gate is ready and the total score is at least 85%. Validate under real load before expanding.
Method sources
Built from primary production, evaluation and risk guidance
The criteria synthesize these references into an actionable business review. Source names and links remain visible so teams can inspect the underlying guidance.
Frequently asked questions
Using the AI agent production readiness checklist
What makes an AI agent production-ready?
A production-ready agent has a bounded job, named owner, representative evaluations, scoped permissions, safe failure behavior, observable traces, controlled cost and human approval for sensitive actions. These controls must be tested in the target environment, not only described in a document.
Is a high score a security or compliance certification?
No. The scorecard is a planning and review aid. A high score does not certify security, privacy, compliance, safety or business performance. Those conclusions require evidence, specialist review and validation against the exact system and jurisdiction.
When should the scorecard be repeated?
Repeat it before the first live pilot, after material changes to prompts, models, tools, data access or permissions, and on a recurring operating schedule. An incident or unexplained quality decline should also trigger a new review.
What evidence should a team keep?
Keep the job definition, owner, evaluation cases and results, permission inventory, tool traces, deployment record, rollback test, approval rules, incident runbook, cost metrics and review decisions. Evidence should be versioned and access-controlled.
From review to pilot
Close the production gaps around one measurable agent job
AI agent development turns the scorecard into architecture, evaluations, integrations and an operated pilot with explicit limits.
See AI agent development services