Multi-agent orchestration patterns that actually work

After building dozens of agent systems, I have strong opinions about what patterns survive contact with reality. Here are the ones I keep coming back to.

Pattern 1: Supervisor with specialists. One agent decides what needs to happen and delegates to specialized agents. The supervisor never does the actual work, it just routes and validates. This separation makes debugging much easier.

Pattern 2: Sequential pipelines with checkpoints. Each agent in the chain produces a well-defined output that the next agent consumes. If something fails, you can restart from the last checkpoint instead of from scratch. Critical for long-running workflows.

Pattern 3: Consensus with fallbacks. For high-stakes decisions, run the same task through multiple agents and compare outputs. If they agree, proceed. If they disagree, escalate to a more capable model or a human. More expensive but much safer.

What does not work: Fully autonomous agent swarms without guardrails. Agents that can spawn unlimited sub-agents. Systems where no single component has a complete picture of what is happening. These all sound cool in theory but create debugging nightmares.

The key insight is that good multi-agent architecture is about constraints, not capabilities. The more you constrain what each agent can do, the more reliable the overall system becomes.