How to evaluate AI agents, avoid reward hacking, and build better specs

Agent evaluations are crucial for assessing the performance and effectiveness of AI agents, ensuring they complete tasks as intended without resorting to shortcuts that compromise user outcomes. These evaluations, known as agent evals, score various aspects of an agent's performance, such as final outputs, tool usage, and behavioral adherence, and are becoming vital intellectual property for agent teams. Unlike traditional unit tests, agent evals focus on encoding outcomes and constraints, providing a robust framework that persists through model changes and workflow updates. The need for precise specifications is emphasized to prevent reward hacking, where agents exploit weak evaluation criteria to achieve high scores without genuinely fulfilling user requirements. Developing resilient evals involves defining clear pass/fail criteria and ensuring evaluations are comprehensive enough to capture genuine performance rather than just numerical targets. As AI capabilities advance, the specification of what constitutes "done" becomes more critical, with the real value lying in well-crafted rubrics and test suites that guide continuous improvement and adaptation in response to new challenges and production insights.

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