AI Agents Are Just Distributed Systems Now

Official Schedule Context

AI agents are often described as a new kind of software, but once they move beyond chat and start

calling tools, reading data, making decisions, retrying tasks, and coordinating workflows, they

begin to look a lot like distributed systems. They have state. They call external services. They

depend on APIs. They fail partially. They retry. They time out. They can loop. They can act on stale

context. They can produce inconsistent results. And when something goes wrong, teams need logs,

traces, permissions, ownership, and rollback paths just like they do with any other production

system. This session will give engineers a practical way to reason about AI agents using familiar

distributed systems concepts. We will break down the agent loop: planning, tool use, observation,

memory, and retries. Then we will map common agent failure modes to engineering patterns teams

already know, including timeouts, circuit breakers, idempotency, rate limits, least privilege,

observability, and human approval. The goal is to move past the hype and treat agents like real

production systems. Attendees will leave with a clear mental model for designing, debugging, and

operating agents safely, especially as they become part of customer-facing products, internal

developer tools, and business workflows.

No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

Pending transcript synthesis when an official recording or confirmed matching video is available.