Session
Taming Rogue AI Agents with Observability-Driven Evaluation
LLM agents often drift into failure when prompts, retrieval, external data, and policies interact in unpredictable ways. This session introduces a repeatable, metric-driven framework for detecting, diagnosing, and correcting these undesirable behaviors in agentic systems at production scale.
Jim Bennett
Principal Developer Experience Engineer
Redmond, Washington, United States
Links
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