Multi-Agent Systems Patterns and Problems: A 2026 Production Guide
Multi-agent systems patterns are the recurring ways multiple LLM agents divide work: orchestrator-worker, supervisor, sequential pipeline, parallel fan-out, swarm, and blackboard. The problems are equally consistent. Coordination costs 58% to 515% extra tokens, failure rates in production run 41% to 87%, and roughly 79% of failures trace to specification and coordination defects rather than weak models. That is the short version. The rest of this guide is the long version, and it is organized around an uncomfortable finding: the strongest production heuristic in 2026 is not to build a multi-agent system until you can show that a tuned single agent cannot do the job. ...