AI agents promise breakthroughs but often deliver operational chaos. Building reliable, deployable systems with unpredictable LLMs feels like wrestling fog – testing outputs alone is insufficient when the underlying workflow is opaque and flaky. How do we move beyond fragile prototypes?

This talk, from the creator of Docker, argues the solution lies *outside* the model: engineering **reproducible execution workflows** built on rigorous architectural discipline. Learn how **containerization**, applied not just to deployment but to *each individual step* of an agent's workflow, provides the essential **isolation and environmental consistency** needed.

Discover how combining this granular container approach with patterns like immutable state management allows us to **contain agent chaos**, unlock effective testing, simplify debugging, and bring essential control and predictability back to building powerful AI agents you can actually ship with confidence.