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Real-world AI agents fail differently than traditional software, silently, with confident hallucinations instead of error codes. In this episode, we explore how AgentOps adapts DevOps principles to handle the unique challenges of LLM-powered systems. You’ll learn:

* Why agent systems require fundamentally different operations than traditional ML models

* How the AgentOps lifecycle handles probabilistic reasoning and semantic failures

* How to implement production-grade observability using MLflow’s tracing, prompt management, and evaluation capabilities

If you’d rather read than listen, the full article (with code, implementation details, and comprehensive examples) is available on Substack:

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