Orchestration, coordination, and AI teams that get things done
A single agent can handle a surprising amount, but some problems genuinely need a team. Multi-agent systems let you break complex tasks into specialized roles, run subtasks in parallel, and build checks and balances into your AI workflows. This category covers the architectures that matter — supervisor-worker patterns, sequential pipelines, autonomous swarms — along with the coordination challenges that make multi-agent systems harder to debug than they look. You will also find guidance on how agents communicate, share state, and avoid the traps that cause cascading failures.
Multi-agent systems assign specialized roles to separate AI agents that coordinate to complete tasks no single agent could handle reliably. The key architectures — supervisor, pipeline, and peer-to-peer — each trade control for flexibility in different ways.
The three dominant agent orchestration patterns are supervisor (central coordinator delegates to workers), sequential pipeline (agents pass output forward in a chain), and swarm (agents communicate peer-to-peer). Each trades control, debuggability, and flexibility in different proportions.
AI agent communication design determines system cost, reliability, and debuggability as much as any individual agent's capability. The two fundamental approaches — message passing and shared state — each have distinct trade-offs, and most production systems use a hybrid.