Developing AI agents often faces the issue where a model appears polite and persuasive but provides factually incorrect information. This happens because standard evaluation systems focus solely on text quality, ignoring the technical side of the agent's operation. For example, a bot might ignore a necessary tool call to check a return policy but still receive high scores for coherence and speech style, masking a fundamental failure in its logic and execution.
The first trap is judging only the final text. Since many evaluators treat conversations as raw text, they miss the function calls the agent actually performs. The solution is implementing deterministic evaluators that compare expected tool calls with the model's actual actions. Forbidden calls should be treated as hard failures rather than stylistic issues, which prevents dangerous scenarios where an agent might promise unauthorized actions or bypass critical business logic during its interaction with the user.
The second problem is using a single metric profile for every conversation. A correct refusal to a prompt injection request should not be penalized with a low relevance score. Using different profiles for various tasks, such as guardrails or standard inquiries, prevents the testing system from flagging correct behavior as a failure. This approach ensures flexibility, where each category of dialogue is evaluated against relevant criteria rather than being forced into a one-size-fits-all spreadsheet.
The third trap is averaging away catastrophic cases, which allows terrible answers to hide behind many successful ones. Individual failures, such as providing false refund guarantees, cannot be offset by a high average score. It is necessary to split metrics into hard requirements, which must pass every time, and graded metrics that have minimum thresholds. This ensures that a single terrible answer is not masked by eleven good ones, maintaining the integrity of the evaluation process.
The fourth issue concerns groundedness without actual data. If a model does not use tools, it has no basis for its claims. The evaluator must be grounded in the results returned by tools during the specific run, not on what the developer hoped would happen. A lack of output should be treated as a failure, signaling that the agent skipped a vital step, making its response unsupported and potentially misleading for the end user.
Finally, it is crucial to avoid treating an unscored metric as a pass. If the evaluation system returns an error or fails to provide a score, it must be treated as a gate failure. Any technical malfunction in the measurement instruments should block the build to prevent untested code from reaching production. Following these rules allows for the creation of a robust testing system that verifies the agent's actual functionality rather than just its ability to sound fluent.
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