Orchestration (Multi-Agent Orchestration)

What Orchestration Means

Multi-agent orchestration is the practice of coordinating two or more AI agents, each with a distinct role, prompt, or tool set, so they complete a task together that a single agent would handle worse or not at all. The coordination is the point — running the same prompt on several models isn't orchestration, it's just redundancy.

Think of it like a newsroom instead of one overworked freelancer: a researcher gathers facts, a writer drafts, an editor checks the draft. Each agent is good at one job, not all of them at once.

The Supervisor/Worker Pattern

The most common structure is supervisor/worker: one agent breaks the overall task into pieces and assigns them to worker agents, then reviews what comes back — retrying a step, reassigning it, or sending a worker back with more specific instructions.

Worker prompts are narrow and specific ("You are a research agent, return five verified facts"). Supervisor prompts are broader and evaluative ("Review the research agent's output for gaps before passing it on").

When It's Worth It

A single, well-prompted agent still matches multi-agent systems on the majority of tasks, and every hand-off between agents adds a chance for context to get lost. Orchestration earns its added cost only when the roles are genuinely distinct — most production setups stay small, usually 2 to 4 agents, since coordination overhead grows faster than the benefit past that.