How Multi-Agent AI Systems Work in Production and When to Use Them

A development team initially built a single AI agent to research, draft, and send reports, but the system grew unwieldy with an overloaded prompt and tool list. Breaking it into coordinated specialised agents resolved the core issues but introduced new challenges around context loss, latency, and compounding errors. The team identified three main coordination patterns: an orchestrator-worker model, a sequential pipeline chain, and an event-driven architecture using Celery and Redis. They advise using a single agent when tasks fit within one context window, and multiple agents when steps can run in parallel, require different instruction sets, or need failure isolation. The key decision framework is asking whether a task is a single job or a pipeline of jobs that would naturally be handed off between people.