Prefer reading instead? The full article is available here.

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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This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aipractitioner.substack.com

Prefer reading instead? The full article is available here.

Real-world decisions often involve conflicting objectives, like boosting sales while avoiding overstock. In this episode, we explore how multi-objective optimization (using NSGA-II with the pymoo library) can model and solve such dilemmas. You'll learn:

* Why traditional dispatch strategies fall short

* How Pareto fronts reveal optimal trade-offs

* How to apply ASF and Pseudo-Weights to guide final decisions

If you’d rather read than listen, the full article (with code, charts, and detailed 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.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aipractitioner.substack.com