Multi-Agent Orchestration

Multi-agent orchestration is the coordination of several AI agents — each with its own role, tools, and context — working together on a task, typically through a supervisor agent that delegates subtasks to worker agents and assembles their results, rather than one agent handling every part of the job in sequence.

A single agent handling a complex task accumulates everything in one context window: every tool result, every dead end, every piece of research, whether or not it's relevant to the step at hand. Splitting the work across specialized agents — a researcher, a coder, a reviewer, each with only the context its role needs — keeps each agent's window focused and lets independent parts of the task run at the same time instead of one after another.

How it works

The most common shape is supervisor-worker: a coordinator agent breaks the goal into subtasks, dispatches each to a worker agent (sometimes a different model tuned for that subtask's needs), and merges the results once workers report back. Workers can run in parallel when their subtasks are independent, or in sequence when one depends on another's output. Some architectures instead use peer agents that hand off directly to each other without a central coordinator. Either way, the defining feature is isolated context: each agent's window holds only what its own subtask needs, not the whole task's history.

Why it matters

The upside is real: parallel subtasks finish in the time of the slowest one instead of the sum of all of them, and a worker's focused context produces better results on its slice than a generalist agent juggling everything at once. The upside is not free: every additional agent duplicates shared context (system prompts, background, tool schemas) that a single agent would only pay for once, and a coordinator that doesn't verify a worker's output before trusting it just moves the risk of a hallucinated or wrong result one level up instead of catching it.

A worked example

Five independent research subtasks, each taking 40 seconds, run sequentially in a single agent: 200 seconds total. Delegated to five worker agents running in parallel, the wall-clock time is roughly the slowest one plus coordination overhead: about 45 seconds — a 4.4x speedup. The cost side is less flattering: if each worker needs the same 2,000-token shared context (role instructions, background, tool definitions) that a single agent would only load once, five workers duplicate that context five times over — 10,000 tokens of overhead versus 2,000 for one agent, a real cost that has to be weighed against the time saved.

How miatz teaches it

Multi-agent orchestration is a capstone-level exercise, built only after learners have a solid single-agent loop working — because the coordination failure modes (a worker's bad result trusted without verification, duplicated context nobody accounted for in the cost estimate) are only visible once there's more than one agent to lose track of. War Room drills include a documented incident where an unverified worker-agent result propagated into a customer-facing answer, because catching that class of failure is the actual skill multi-agent systems demand.