From prototype to production: a practical checklist for AI agents
Eight questions we use to take an AI agent from an impressive demo to a dependable system your team can rely on.
Deomigo Engineering · · 5 min read
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Building an AI agent demo takes an afternoon. Building one your team can rely on every day takes deliberate engineering. This checklist covers the questions we find most useful when moving an agent from prototype to production.
1. Define the job before the model
Write down the task the agent owns, what “done” looks like, and which decisions must stay with people. A narrow, well-specified job beats a general assistant that does many things unpredictably.
- What inputs does the agent receive, and from where?
- Which tools or APIs may it call — and which are off-limits?
- Which actions need human approval before they take effect?
2. Build an evaluation set early
Collect realistic examples — including awkward edge cases — and define how you will judge outputs. Even fifty carefully chosen cases turn “it seems better” into a measurable signal, and they become your regression suite.
3. Treat tools as contracts
Tool definitions are an interface. Give them clear names, strict input schemas and helpful error messages. Validate every argument the model produces before acting on it, and make write operations idempotent where possible.
4. Design the human-in-the-loop path
Decide where people review, approve or override. Good escalation includes context — what the agent saw, what it tried and why it stopped — so the person picking it up doesn't start from zero.
5. Make it observable
Trace each run: prompts, tool calls, latencies, token usage and outcomes. When something goes wrong in production, you want to replay exactly what happened rather than guess.
6. Choose models deliberately
Use your evaluation set to compare models on your real task. Often a smaller, faster model handles most steps, with a more capable model reserved for the hard ones. Revisit the choice as models and prices change.
7. Plan for failure
- Timeouts and retries with backoff for every external call.
- Step and cost limits so a confused agent can't loop forever.
- Safe fallbacks: hand off to a person rather than guess.
8. Roll out gradually
Start in “suggest” mode, where the agent drafts and people decide. Measure agreement and outcomes, then widen autonomy for the cases where it has earned trust.
Start small. Prove value. Build trust. Scale intelligently.
None of this is exotic — it's the same discipline that makes any production system dependable, applied to a component that happens to be probabilistic.