Taking AI agents to production: a practical readiness checklist

AI agents are moving quickly from experiments to real workloads. An agent that can read a request, look up records, call an API and draft a response can absorb a lot of coordination work. It can also make a lot of mistakes very quickly if it is released without the right preparation.

This checklist reflects the questions we work through with clients before an agent is allowed to act on anything that matters. None of it is exotic. Most of it is ordinary software engineering discipline applied to a new kind of component.

1. Is the job clearly defined?

Agents perform best with a narrow, well-understood objective. "Help customers" is not a job. "Resolve address change requests for existing customers and update the CRM" is. Write down the inputs the agent will receive, the outcome it should produce, and what it must never do.

• A one-sentence goal that a new staff member would understand
• A list of in-scope and out-of-scope request types
• A definition of done the agent can check against

2. Are tool permissions minimal?

Every tool an agent can call is a capability that can be misused, whether through a model error or a malicious prompt hidden in an email or document. Give the agent only the tools the job requires, scope API credentials to the minimum data and actions, and prefer read-only access wherever possible.

3. Which actions need a human?

Classify every action by impact. Looking up an order status is low impact. Issuing a refund, changing bank details or sending an external email is not. High-impact actions should pause for human approval, at least until the agent has a proven track record.

4. Do you have an evaluation set?

You cannot improve what you cannot measure. Build a set of realistic test scenarios, including awkward edge cases and adversarial inputs, with expected outcomes. Run it on every change to prompts, tools or models. Score not only the final answer but the path the agent took to get there.

5. Can you see what the agent did and why?

Every run should produce a trace: the input, each reasoning step, each tool call with its parameters and results, and the final output. When something goes wrong, and it will, traces turn a mystery into a fixable bug. They also provide the audit trail that risk and compliance teams need.

6. What happens when the agent is unsure?

Good agents know their limits. Design explicit fallback behaviour: ask a clarifying question, hand off to a human queue with full context, or decline politely. An agent that confidently guesses is far more dangerous than one that escalates.

7. Have you run it in shadow mode?

Before granting real autonomy, let the agent run alongside your team. It proposes actions, people execute or reject them, and you compare. Shadow mode surfaces real-world failure patterns that no test set will predict, without any customer impact.

8. Do you know what it costs?

Agents can loop, retry and call models many times per task. Track cost and latency per completed task from day one, set hard limits on steps and spend, and alert when they are exceeded.

Putting it together

None of these steps require a particular framework or model. They are design decisions that make an agent predictable, observable and safe to expand. Agents built this way earn trust gradually, and that trust is what allows them to take on more valuable work over time.