This text outlines the critical role and methodology of automated evaluations for assessing AI agents, which are notably difficult to measure due to their autonomous and multi-turn nature. The source identifies essential components of a robust system, including diverse grader types—such as code-based, model-based, and human review—to verify both process and final outcomes. It provides tailored strategies for different agent categories, including coding, conversational, research, and computer use models, while emphasizing the need for isolated environments to ensure results are reproducible. Developers are encouraged to adopt eval-driven development early to distinguish between genuine capability improvements and accidental regressions. Ultimately, the text argues that a holistic understanding of performance requires combining automated suites with real-world production monitoring and human calibration.============== Code content percentage: 4.08% Total text length: 39489 characters 🔗 Original article: https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents📋 Monday item: https://omril321.monday.com/boards/3549832241/pulses/10971529813

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