Your AI Agent Evaluation Harness Is Lying to You
Your AI Agent Evaluation Harness Is Lying to You Your eval suite is green and your agent is still doing something dumb in production. Both of those things can be true at the same time, and the reason is uncomfortable: AI agent evaluation that only scores the final answer is measuring the wrong thing. An agent can pass every check you have while accessing unauthorized resources, leaking private…
Your AI agent evaluation harness may be providing misleading results. The evaluation suite focuses solely on the final answer, failing to detect underlying issues such as unauthorized resource access, private context leakage, or irreversible side effects. Even if the final response appears correct, the trace reveals that the agent executed expensive searches, accessed restricted data, or performed risky actions.
This "final answer bias" leads to a false sense of security, as the evaluation suite does not detect these intermediate failures. To address this issue, implement a comprehensive metrics framework covering various aspects of agent behavior, including task outcome, trajectory quality, tool call correctness, cost and token efficiency, safety and permissions, latency, and human judgment.
Utilize trace-based evaluations to capture every tool call, argument, and intermediate step, allowing for accurate audits and identification of problematic behavior.
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