Multi-Agent Orchestration: A Guide to Patterns That Work

Every multi-agent article opens with the same pitch: your single agent is failing, you need five agents, here are the patterns. The internet is full of pattern catalogs. What it lacks is honesty about when multi-agent orchestration actually helps -- and when it makes everything worse. This article is different. We will cover the patterns, but we will start with the question nobody asks: do you actually need multi-agent? Then we will walk through the four patterns that cover 90% of production use cases, show you framework-agnostic Python code, and do the cost math that most guides skip. This is part of the Building Production AI Agents series. If you have not read Why Your AI Workflow Breaks at Scale yet, start there -- it covers the three walls (monolith prompts, tool explosion, state amnesia) that push teams toward multi-agent in the first place. When You Actually Need Multi-Agent Before picking a pattern, confirm you have a multi-agent problem. Most teams jump to multi-agent because