Agent Readiness Checklist.
Fourteen operational checks that decide whether your agent reaches production or stalls in the demo graveyard. The model is rarely the reason. The harness around it usually is.
- 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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What the fourteen checks cover, why demos pass and launches stall, and what the download actually costs.
It 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 a team skips it. The items cover which decisions the agent makes alone, what its tools can reach, where its data may be processed, how a run gets traced, and what happens when it needs 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. The questions that stall a deployment are which decisions the agent may make alone, what its credentials can reach, where traces and embeddings may live, and who answers when it gets something 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 all read differently depending on whether the weights sit in your own rack or 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. It costs an email address and nothing else. The download link appears the moment you submit the form, the newsletter arrives at most four times a month, and one click unsubscribes you from it. There are no tracking pixels on the page.
Nothing automatic. You get the file and the newsletter. If you want the same discipline applied to your own build, the fixed-scope Agentic Pilot runs four weeks on one production use case from $15,000 and includes 30 days of post-launch support. Otherwise a written brief or a discovery call sorts whether anything is worth doing at all.