Prefer reading instead? The full article is available here. The podcast is also available on Spotify and Apple Podcasts. Subscribe to keep up with the latest drops.
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:
👉 Like this kind of content? Subscribe to get future articles and episodes delivered straight to your inbox as soon as they’re published.
