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AI agents have evolved beyond simple question-answer systems into complex, multi-step entities that plan, reason, retrieve information, and execute tools across dynamic conversations. This evolution introduces significant evaluation challenges. Unlike traditional machine learning models with static inputs and outputs, AI agents operate in conversational contexts where performance depends on maintaining

TL;DR Evaluating Retrieval-Augmented Generation (RAG) systems requires specialized tooling to measure retrieval quality, generation accuracy, and end-to-end performance. This comprehensive guide covers the five essential RAG evaluation platforms: Maxim AI (end-to-end evaluation and observability), LangSmith (LangChain-native tracing), Arize Phoenix (open-source observability), Ragas (research-backed metrics framework), and DeepEval (pytest-style testing)