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Building effective evaluations (evals) for AI agents requires combining multiple grader types—code-based, model-based, and human—to assess both outcomes and interaction quality. Start with 20-50 realistic tasks from actual failures, create unambiguous success criteria, and design balanced problem sets. Key practices include maintaining stable test environments, reading transcripts to validate graders, monitoring for eval saturation, and treating evals as living artifacts requiring ongoing maintenance. Different agent types (coding, conversational, research, computer use) need tailored evaluation approaches, but all benefit from early investment in evals to accelerate development and prevent regressions.

Demystifying evals for AI agents

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