Multi-Agent Orchestration in plain English.

Multi-agent orchestration is the practice of coordinating several AI agents, each responsible for a narrower piece of a larger task, instead of asking one model to do everything. A typical setup: a "planner" agent breaks a request into steps, a "researcher" agent gathers information, a "coder" agent writes the implementation, and a "reviewer" agent checks the output before it ships — each running its own prompt, tools, and sometimes its own underlying model. The appeal is that narrower jobs are easier for any single agent to do reliably, and different sub-tasks can use different models matched to cost and capability. The cost is complexity: more agents means more places for errors to compound, more difficulty debugging why something went wrong, and real coordination overhead. Frameworks like LangGraph, CrewAI, and AutoGen exist specifically to manage this coordination. In 2026, multi-agent systems work well for well-defined pipelines and still struggle with tasks that need real-time back-and-forth judgment between the agents.