Evaluating AI agents is more complex than checking final answers. This analysis breaks down the key dimensions: task completion, result quality, trajectory validity, and tool call correctness. For example, an agent asked to summarize an API's latest usage might succeed through search, read, or webfetch paths—all valid if the source is authoritative. The challenge is defining what 'correct' means when multiple strategies work. Teams need to move beyond simple pass/fail metrics and consider process-level evaluation, including whether the agent used tools appropriately and followed logical steps. This is especially critical as agents are deployed in production where reliability and interpretability matter. The post offers a framework for thinking about these trade-offs, making it a valuable resource for AI engineers and product teams.
A practical guide to evaluating AI agents, covering task outcomes, result quality, execution trajectories, and tool call accuracy. Understand why multiple valid paths exist and how to design robust evaluation frameworks.