Orchestrator Worker Pattern

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. ...

Letting agents do agent work means moving from prompting a single model to orchestrating a team of specialized AI workers that collaborate toward one outcome. The real bottleneck in 2026 is not model capability — it is coordination. In this guide, you will learn how to design agent teams, choose the right collaboration patterns, structure handoffs, and govern human-on-the-loop workflows that actually ship. What Does It Mean to Let Agents Do Agent Work? For years, “automation with AI” meant one model, one prompt, one answer. Andrew Ng’s research reframed this: a relatively weak GPT-3.5 model driven by an iterative agentic workflow scored 95.1% on HumanEval, while GPT-4 zero-shot scored only 67%. The lesson is not that bigger models are obsolete — it is that how you structure the work often matters more than raw model size. ...