Every team does the same thing. Agent behaves weirdly in production - retries endlessly, drops tasks, returns confident nonsense. First reaction: swap the model, tweak the prompt. It's never the model. Multi-agent systems aren't an AI problem. They're a distributed systems problem with an AI layer on top. And most teams skip all the boring infra - state management, observability, failure handling, routing - and pay for it later. We'll go through 8 agentic patterns (supervisor/worker, swarm, reflection, human-in-the-loop and more), and for each one: what the pattern does, where it breaks, and what infra you actually need before shipping it to production.
AI Architect at Payoneer | Full Cycle Development Expert | Public Speaker | Open Source Contributor |