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TL;DR Production-ready LLM applications require comprehensive evaluation frameworks combining automated assessments, human feedback, and continuous monitoring. Key components include clear evaluation objectives, appropriate metrics across performance and safety dimensions, multi-stage testing pipelines, and robust data management. This structured approach enables teams to identify issues early, optimize agent behavior systematically,

TL;DR Human-in-the-loop evaluation fills critical gaps that automated evaluators miss in agentic AI systems. This guide explains how to integrate Human-in-the-loop with machine evaluators, distributed tracing, and production observability. You'll learn when to route interactions to humans, how to structure effective rubrics, and how to convert feedback

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