Why Agent Evaluation Is Harder Than Model Evaluation

Short summary

The author, building an open-source agent evaluation tool called AgentEval Forge, explains why evaluating AI agents is fundamentally harder than evaluating models. Model eval asks if the answer is good; agent eval must ask if the system behaved well enough to trust. The path matters as much as the endpoint because cost, safety, and trust live in the trajectory, not just the final output.

• •Agent evaluation must score workflow behavior, not just final answers
• •An agent can produce a correct result while making poor tool choices, overspending tokens, or crossing safety boundaries
• •The author is building AgentEval Forge, an OSS evaluation lab for agents

Generated with AI, which can make mistakes.