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Attend this 45-minute tech talk to learn how to evaluate and improve AI agent reliability using advanced assessment frameworks. Discover why traditional evaluation methods fail to detect hidden agent failures that occur during the reasoning process, even when final outputs appear correct. Explore how agents can drift from goals, make irrational plan jumps, or misuse tools while still producing seemingly acceptable results, leading to increased compute costs, higher latency, and brittle production behavior. Learn about the Agent GPA (Goal-Plan-Action) framework from the open-source TruLens library, which provides scalable insights into agent decision-making processes. Examine benchmark results showing 95% error detection rates compared to 55% with baseline methods, and 86% accuracy in pinpointing error locations versus 49% with traditional approaches. Understand how human reviewers achieved 100% detection of internal agent errors using the GPA framework on the TRAIL/GAIA dataset. Master techniques for inspecting agent reasoning steps, identifying issues like hallucinations, problematic tool calls, and missed actions to ensure your AI agents are truly production-ready.
