AI agent evaluations are part of the product
A team builds an agent, gives it a few representative questions in a test chat, and watches it produce useful The post AI agent evaluations are part of the product appeared first on The New Stack .
The process of evaluating an AI agent is crucial before shipping the product, as a demonstration of a single successful run does not guarantee consistent behavior under different conditions. Evaluation should become an integral part of the delivery process, ensuring the agent consistently meets the needs of users and operators.
To establish correct behavior, define the agent's tasks, their limits, and outcomes that fall outside acceptable boundaries. For instance, a support agent's job might involve answering billing questions using records from the correct account and citing the relevant policy. The agent should acknowledge when it cannot find a policy or request missing account data, rather than presenting an unsupported answer as fact.
Separate the result from the process that produced it. An agent can provide the correct answer using the wrong document or complete a task by calling an unnecessary tool, both of which may seem successful in a transcript but hide underlying weaknesses. Start with a small set of observable requirements for each job, such as required facts and named sources.
Test with real user tasks, such as support tickets and workflow logs, rather than generic prompts. Include scenarios with unclear instructions or missing data, as well as unusual but valid requests. Agents operate across turns, so some scenarios should involve multiple exchanges, such as requesting an account change, providing the missing identifier, and confirming the change.
Capture the entire execution path, including the request, system instructions, model and application build, prompt and retrieval configuration, tool schemas, retrieved sources, tool calls, permission checks, and the final response. This trace should verify that the agent remains within the correct tenant and customer account, identifies the approved policy source and cited records, and shows that a write action was confirmed by the user and passed server-side permission checks.
By following this repeatable evaluation system, the product can be released with confidence that the AI agent will consistently behave as expected, and any failures can be traced back to a specific cause.
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