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Agent orchestration

Agent orchestration is the coordination of multiple AI agents working on shared tasks: routing work to the right agent, handing off context between steps, preventing two agents from editing the same resource, and logging every decision for human review. It is what turns a set of individual agents into a system that can be trusted with real work.

Updated 8 Jul 20263 min readBy fromHello
Key takeaways
  • Agent orchestration covers four jobs: routing tasks, handing off context, preventing write conflicts, and logging decisions.
  • Without an orchestrator, parallel agents duplicate work and overwrite each other — coordination is what makes multi-agent output trustworthy.
  • A decision log with rationale is what lets a human audit, redirect, or rewind autonomous work.

How does agent orchestration work?

An orchestration layer sits above the individual agents and handles four jobs. It routes each task to the agent best suited for it. It manages handoffs, passing context from one agent to the next so work does not restart from zero. It prevents conflicts — typically with locks or task ownership, so two agents never write the same resource at once. And it records every decision in a log a human can read. Anthropic's multi-agent research system and OpenAI's Agents SDK both formalize this as an orchestrator-worker (or manager) pattern: one coordinating agent delegates to specialists and synthesizes their results.

Four building blocks of agent orchestration.

Why do multiple agents need an orchestrator?

Because parallel autonomy fails without coordination. Two agents that both edit the same email template produce a mangled template. Two agents that both message the same customer produce spam. An orchestrator serializes writes, deduplicates work, and keeps every agent aligned on the same goals. This is also part of what separates agents from chatbots: a chatbot answers you, while orchestrated agents act on shared systems — which only stays safe if something coordinates them.

What does agent orchestration look like in a growth platform?

fromHello is one concrete instance. An Orchestrator coordinates an AI growth team of eight specialists — strategy, experimentation, lifecycle, paid channels, CRO, data, engineering, copy. The agents share a Truth Pack (ICP, brand voice, goals) as common context, the Orchestrator prevents two agents from writing the same segment or journey at once, and every decision is logged with its rationale so the operator can audit, redirect, or rewind.

What should a human be able to audit?

Everything the agents shipped, and why. A usable decision log records the action, the agent that took it, the inputs it relied on, and the reasoning — not just a timestamp. That is what makes human-in-the-loop review practical: you approve or reject proposals up front, and once you auto-approve a surface, the log remains your way to trace what happened. Orchestration without an auditable trail is just automation you cannot inspect.

FAQ

Common questions

  • What is the difference between agent orchestration and a multi-agent system?

    A multi-agent system is the set of agents itself; orchestration is the coordination layer that makes them work as one — routing tasks, sharing context, preventing conflicts, and logging decisions. You can have multiple agents without orchestration, but not for long without collisions.

  • Is agent orchestration the same as workflow automation?

    No. Workflow automation executes steps you defined in advance. Orchestrated agents decide their own steps within constraints, and the orchestrator coordinates those decisions rather than scripting them.

  • How does an orchestrator prevent two agents from overwriting each other?

    Common mechanisms are resource locks, task ownership, and serialized writes: an agent claims a resource — a segment, a template, a journey — before editing it, and other agents must wait or work elsewhere.

  • Does agent orchestration remove the human from the loop?

    No. Good orchestration makes human oversight cheaper: proposals are approved before they ship, and the decision log lets you audit or rewind anything you delegated. Autonomy is a setting, not the default.

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