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A/B testing has evolved from a simple website optimization technique to a critical methodology for evaluating and improving AI agent performance. As enterprises deploy increasingly sophisticated agentic AI systems, traditional testing approaches often fall short. AI agents are transforming A/B testing from a blunt instrument into a precision

LLMs are non-deterministic by nature. The same prompt can produce different outputs across runs, and subtle changes in retrieval pipelines, model versions, or prompt templates can quietly degrade quality without triggering traditional error alerts. As AI agents move from prototypes to production, LLM evaluation platforms have become foundational infrastructure

LLM hallucinations aren’t random; they’re measurable. This guide breaks down six core metrics and explains how to wire them into tracing and rubric-driven evaluation so teams can diagnose failures fast and ship reliable AI agents with confidence.