How to Evaluate an AI Agent Before Putting It Into Production
Stop testing agents with a few happy paths. Use a repeatable evaluation set that covers task success, tool use, failures, safety, latency, and cost.
Stop testing agents with a few happy paths. Use a repeatable evaluation set that covers task success, tool use, failures, safety, latency, and cost.
Evaluate the complete workflow: input, tool decisions, state transitions, side effects, and final business outcome.
The practical question behind this topic is how to turn an AI capability into a dependable product component. Start by defining the job in terms of an input, a useful transformation, and an observable outcome. Avoid designing around the model first. The model is one component inside a workflow that also includes application state, tools, permissions, retries, logging, and user feedback.
For an article about How to Evaluate an AI Agent Before Putting It Into Production, a useful first exercise is to write the workflow as a sequence of states. Identify what the user provides, what the model needs to know, what information must come from a trusted system, which operations can change data, and what happens when the model is uncertain. This makes hidden assumptions visible before implementation.
A model can interpret a request, classify information, draft a response, or choose between allowed capabilities. It should not become the source of truth for billing, permissions, account ownership, inventory, or destructive actions. Those rules belong in application code. A useful architecture therefore has a clear boundary: the model proposes an action, a typed tool validates it, the application authorizes it, and the system records the result.
This boundary also makes testing easier. Instead of asking whether a model response sounds good, test whether the right tool was selected, whether arguments were valid, whether authorization was enforced, and whether the workflow reached the expected terminal state.
AI systems fail differently from ordinary deterministic services. A response can be syntactically valid but semantically wrong. A tool can time out after the external service has already accepted the request. Retrieval can return stale information. Context can become too large. A retry can accidentally repeat a side effect.
Use explicit limits for turns, latency, token usage, and tool calls. Give side-effecting operations idempotency keys where possible. Store enough trace information to reconstruct the run without storing unnecessary private data. When the system cannot safely continue, return a useful fallback or ask for human intervention rather than silently guessing.
Before launch, collect representative cases rather than relying on a handful of demos. Include normal requests, ambiguous requests, missing data, malformed tool arguments, permission failures, provider errors, and adversarial inputs. Track task success, tool accuracy, latency, cost, and recovery rate. Re-run the same cases after changing the model, prompt, retrieval system, or tool schema.
The goal is not to make the agent appear autonomous. The goal is to make a useful workflow reliable enough that a customer can trust its outcome. Start narrow, keep business rules deterministic, measure the complete job rather than only the model response, and expand the tool surface only when the existing workflow is demonstrably stable.
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Written by
Kirtesh Admute
Founder
Kirtesh Admute is the founder of IndieFounder, a platform for founders, builders, and people curious about technology. He writes about AI, startups, software, product building, and the lessons that come from building in public.
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