Self-Improving Evaluations for Agentic RAG - Tracing and Feedback Loops

Learn to build self-improving evaluation systems for agentic RAG applications through practical tracing and feedback loop implementation. Discover how to trace multi-step reasoning plans using open-source tooling, identify hidden failure modes including tool misuse and hallucinated context, and measure beyond simple accuracy to include tool-call correctness, trajectory coherence, and multi-turn consistency. Explore improvement strategies such as routing across data sources, fixing context injection issues, and refining evaluation prompts to create systems that enhance their own judgment capabilities over time. Gain hands-on insights from real-world deployments with specific guidance on instrumentation, dataset creation, and threshold setting to make agentic systems observable, accountable, and continuously improving.