Evaluating agents

This blog post provides a comprehensive guide on evaluating the quality and accuracy of agentic systems, which are complex systems that can perform tasks autonomously. The authors highlight the importance of running evaluations to detect and debug issues before they impact users, and provide practical strategies for choosing evaluation metrics, building block: the augmented LLM, prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, fully autonomous agents, best practices, and next steps. The post covers various types of agentic systems, including simple augmented large language models (LLMs), fully autonomous agents, and more complex systems that combine multiple components. It also discusses the challenges of evaluating these systems, such as determining the right set of scorers, handling subjective or contextual feedback, and incorporating domain-specific knowledge. The post concludes by emphasizing the importance of refining or replacing scorers over time to learn more about the real-world behaviors of agentic systems at scale.

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