Taking AI agents from prototype to production

An agent is a controlled loop

An AI agent repeatedly observes context, chooses an action, uses a tool and evaluates what happened. In production, the surrounding control loop matters more than the theatrical ability to ‘reason’. The system must define which tools are available, what each tool may change and when a person must approve or take over.

Begin with a bounded job such as triaging a support case or drafting a change plan. Open-ended autonomy makes evaluation vague and expands the security boundary before the team understands normal failure modes.

Separate planning from authority

A model may propose an action without receiving permission to execute it. Use scoped credentials, allow-listed operations and explicit approval for financial, destructive or customer-facing changes. Validate tool inputs in ordinary code; never rely on the model to enforce its own policy.

Make actions idempotent where possible and attach a unique operation identifier. Retries are inevitable, and a repeated request should not create a second invoice, deployment or message.

Evaluate trajectories, not just answers

Agent quality includes whether it chose the right tool, used the correct arguments, stopped at the right time and recovered from an error. Build scenario tests from real tasks and score the complete trajectory. Include missing data, ambiguous instructions, tool outages and hostile content.

Run new versions in shadow mode or with limited authority before expanding access. Review traces for unnecessary steps, hidden loops and cases where a human correction should become a permanent test.

Operate agents like software

Persist state outside the model, set time and cost limits, and emit structured events for every decision and tool call. Provide a kill switch and a clear owner. Sensitive context needs retention rules and redaction, while external content must be treated as untrusted input.

A dependable agent is usually less autonomous than its demo. That is a strength. Clear boundaries make it possible to increase responsibility gradually as evidence accumulates.

Frequently asked questions