As AI agents become more autonomous and interconnected, many of the hardest engineering problems are starting to resemble classic distributed systems challenges rather than traditional application development problems.

In production environments, multi-agent systems introduce orchestration complexity, retry amplification, context propagation issues, non-deterministic execution paths, observability gaps, and cascading failure patterns that closely mirror the behavior of large-scale distributed systems.

This session explores how real-world agentic AI workflows begin to inherit distributed systems failure modes as they scale, and what engineering teams can learn from decades of distributed systems design principles. We will examine practical production lessons around orchestration reliability, tracing, concurrency control, retry handling, state management, and operational debugging for AI agent architectures.

Attendees will leave with a practical framework for thinking about AI agents as operational systems rather than isolated AI components, along with concrete strategies for building more reliable, observable, and scalable agent-based applications.