Take AI agents from prototype to production

A prototype proves that an agent can complete a prepared path. Production requires evidence that it behaves acceptably when data is missing, tools fail, permissions change, or a user asks for something outside the task.

The move is an engineering and operating decision. It is not a larger version of the demo.

Freeze what you are testing

An agent includes more than a model. Record the versions of its instructions, tools, model deployment, data connections, policies, evaluation set, and application code.

Without that record, a failed test cannot be reproduced and a successful test does not identify what is ready to release.

Test the workflow, not only the response

A production evaluation should cover the complete task:

• whether the agent selected an allowed tool
• whether it used the right source
• whether arguments were valid
• whether the action matched the user’s authority
• whether an uncertain case escalated
• whether the final state was recorded

Build cases from normal work, known failures, edge conditions, and deliberate misuse. Set thresholds before reading the results.

Make failure safe

Tools and services will time out, return partial data, reject access, or change their response. Decide what the workflow does in each case.

A safe default may be to stop, preserve the current state, and send the evidence to a person. Automatic retries need limits and idempotent actions so one request does not create several changes.

Write permissions deserve a separate review. Start with read-only access where the use case allows it.

Put identity and policy around every action

Use workload identities and least-privilege roles rather than shared secrets. Validate tool inputs and outputs, and enforce policy outside the model prompt.

Human approval needs a defined point in the workflow. Show the reviewer the proposed action, source, policy result, and effect so approval is informed.

Add traces, evaluation, and operations

Capture traces that connect the request, tool calls, policy checks, result, and final action. Redact sensitive content before it reaches monitoring systems.

Run repeatable evaluations in delivery pipelines and watch production signals such as task completion, error rate, latency, tool failure, escalation, and cost. Set owners for alerts and define when a release is paused or reversed.

Microsoft’s Foundry agent development lifecycle covers versioning, tracing, evaluation, publishing, and monitoring. Check current support for the agent type and features you plan to use.

Prepare people and support

Users need to know what the agent can do, what it cannot do, and how to challenge or escalate a result. Support teams need the trace identifier, known failure modes, and a path to disable risky actions.

A production owner should review changes to the model, tools, prompts, data, permissions, and business rules. Adoption without change ownership becomes an unmanaged dependency.

Release in measured stages

Begin with a narrow group and one defined task. Compare results with the previous process, including exception volume and review effort.

Expand only when the evidence supports a wider authority or audience. A faster response is not enough if the workflow creates more corrections, hides decisions, or moves risk to another team.

The Odie Bot case study is useful evidence for a deployed conversational service. It does not establish that every current Agent Factory feature or production control described here was part of that delivery.