Self-Improving Evaluations for Agentic RAG

Learn to build robust evaluation frameworks for agentic RAG systems in this comprehensive webinar that addresses the unique challenges of evaluating AI agents that reason through multi-step processes. Discover practical approaches to trace complex multi-step plans using open-source tooling while identifying critical failure modes including tool misuse and hallucinated context that traditional evaluation methods often miss. Master techniques for quantifying system performance beyond simple per-turn accuracy by measuring tool-call correctness, trajectory coherence, and multi-turn consistency across extended interactions. Explore advanced improvement loops that enable systems to route intelligently across multiple data sources, optimize context injection strategies, and refine evaluation prompts to create self-improving evaluation capabilities. Examine real-world deployment examples and gain actionable guidance on implementing proper instrumentation, curating effective datasets, and establishing meaningful performance thresholds for production agentic systems. Develop a complete blueprint for making agentic RAG systems observable, accountable, and capable of continuous self-improvement through sophisticated evaluation methodologies.