Agent Readiness Checklist
Fourteen checks decide whether your agent reaches production. Most concern the harness, the controls around the model, not the model itself.
- Name the decisions the agent makes alone, and the ones it may only propose
- Draw the tool access boundary across read, write, and whether the write reverses
- Sandbox the blast radius so the worst outcome stays survivable
- Data residency and regulatory scope (PDPL, DIFC, ADGM, GDPR, HIPAA)
- Choose the model for the task rather than for the brochure
- Retrieval and the citation surface, enforced in code rather than in a prompt
- Build the eval suite before launch, on 100 to 200 real tasks
- Design the escalation path to a named human queue with a response time
- Trace every run end to end, including every tool call, retry, and token count
- Cost guardrails per run and per tenant, with a cap on steps
- Audit logging and SIEM hand-off into the pipeline security already runs
- Name the failure modes and the guardrail that catches each one
- Version prompts, models, and tool definitions in Git
- Write the off-ramp for every external dependency
Residency and audit named explicitly
Written from production deployments
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Agent readiness, answered
What the checks cover and why launches stall.
An agent readiness checklist is the list of operational decisions a team settles before an AI agent touches production. This one runs to fourteen items, and each covers one thing that costs months when skipped. The items cover which decisions the agent makes alone, what its tools can reach, and where its data may be processed. Others cover tracing and the hand-off to a human. Almost every check concerns the harness around the model rather than the model itself.
Because the demo tests the model and the launch tests everything around it. Four questions stall a deployment. Which decisions may the agent make alone, and what can its credentials reach? Where may traces and embeddings live, and who answers when it goes wrong at three in the morning? Those look like paperwork during the demo and become the whole project afterwards.
Both. Where inference runs changes the weight of a few checks rather than the list itself. Data residency, model selection, audit retention and the vendor off-ramp read differently when weights sit in your own rack rather than behind a vendor API. The rest, covering decision authority, tool access, sandboxing, retrieval, evals, escalation, tracing, cost caps, failure modes and versioning, hold either way.
Yes. The full checklist is open to read, with no email and no sign-up. It prints to a clean PDF from the browser, and there are no tracking pixels on the page.