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Evaluating AI agents is essential for reliability. Real-world interactions expose non-determinism, model updates, and hallucinations, which degrade trust without rigorous checks. A structured evaluation approach (pre-release and in production) helps quantify quality, prevent regressions, and align systems to human preference. Maxim AI provides unified capabilities across offline testing, online evaluations,

TL;DR * RAG systems combine retrieval and generation; evaluation must assess both components. * Retrieval quality hinges on recall, precision, relevance, and timeliness of sources. * Generation quality requires grounding, faithfulness, clarity, and low hallucination rates. * Judge reliability improves with mixed methods: LLM-as-a-judge, programmatic checks, and human review. * Use Maxim’s offline

The deployment of AI applications in production environments has accelerated dramatically, with organizations across industries racing to integrate large language models (LLMs), voice agents, and retrieval-augmented generation (RAG) systems into their workflows. However, this rapid adoption has exposed a critical challenge: without rigorous evaluation frameworks, AI applications can fail unpredictably,