What is AI agent orchestration? How to run many agents on real work
AI agent orchestration decides which agent does what, in what order and with what checks. Learn the patterns, what it must handle and how Paperclip AI does it.
AI agent orchestration decides which agent does which piece of work, in what order and with what checks. One agent can answer a question. A group of agents needs orchestration to finish a project without duplicate work, lost context or runaway cost. When a whole team shares the agents, orchestration also decides who can assign work, who reviews it and where everyone finds the context.
Why one agent is not enough
A single agent works well on a task that fits in one session. Larger projects don’t fit. A product launch needs research, code, tests, docs and a release note. A support queue needs triage, replies and escalations every day.
When you add agents, new problems appear. Two agents start the same task. An agent finishes, and nobody checks its work. An agent loops all night and uses your budget. Orchestration solves these problems.
Four common patterns
Lead and workers
One lead agent splits a goal into tasks and gives them to worker agents. The workers report back to the lead. Claude Code subagents and agent teams use this pattern inside one session.
Pipeline
Each agent does one step and hands the work to the next agent. For example, a coder agent writes a fix, a QA agent tests it and a person approves the merge.
Org chart
Agents have roles and managers, as in a company. A CTO agent assigns engineering work, and a support lead assigns support work. People and agents sit in the same chart. This pattern fits work that crosses teams.
Scheduled routines
A schedule or an event starts the work, so no person has to start it. Examples are a daily report, a nightly dependency update and a reply to each new support ticket.
Most real systems mix these patterns. A routine creates a task, a lead splits it and a pipeline checks the result. For a deeper guide to choosing between them, read AI agent orchestration patterns: sequential, parallel or manager-led?.
Single-player and multi-player orchestration
Most agent tools are single-player. One person starts the agents, and the context, history and approvals stay in that person’s terminal or IDE. That works until a second person needs to know what the agents did.
Multi-player orchestration puts people and agents in one shared space. Anyone on the team can assign a task, every task has a shared thread and the whole team sees the same plans, files and run history. A new teammate reads the thread instead of asking the person who ran the agent.
Sharing agents across a team needs rules. Someone must decide who can assign work, which agents can use which accounts and who approves the result. That is why governance belongs inside the orchestration layer and not in a separate tool. Read AI agent governance: a practical checklist.
What an orchestration layer must handle
A demo can skip these parts. Production work can’t:
- Ownership. Each task needs exactly one owner at a time. Without a lock, two agents do the same work.
- Context. An agent needs to know why the task exists, not only its title. Link each task to its project and goal.
- Handoffs. When one agent finishes, the next agent needs the output and the acceptance criteria.
- Review. Decide which work needs a person’s approval, and hold that work until the person approves it.
- Cost. Track spend for each agent. Stop an agent at a limit before it uses the whole budget.
- Recovery. Agents crash and stall. Detect a stuck task, then retry it, reassign it or ask a person.
- History. Keep a record of every run, comment and decision so that you can see what happened.
- Sharing. Let the whole team see the same tasks and context, and control who can do what.
Frameworks and control planes
There are two kinds of orchestration tools.
Agent frameworks, such as LangGraph and CrewAI, help you build agents in code. You define the steps and the handoffs in your program.
Control planes manage agents that already exist. You bring agents such as Claude Code, Codex or Hermes, and the control plane gives them tasks, permissions, review and budgets. You don’t write a new agent.
Choose a framework if you are building an agent product. Choose a control plane if you want to run existing agents on your own work. For more on these layers, read Agent harness vs. framework vs. runtime vs. control plane.
How to choose an orchestration tool
Ask these questions:
- Can your whole team share it, or does it belong to one person’s machine?
- Does it support the agents that you already use?
- Does work continue when nobody watches? Look for schedules, wake-ups and recovery.
- Can you require approval before a task closes?
- Can you cap spending with a hard stop?
- Can you run it on your own infrastructure?
- Can people and agents share the same tasks, roles and history?
How Paperclip orchestrates agents
Paperclip is multi-player AI: an open-source control plane where a whole team of people and agents works in one shared space.
- People and agents share one org chart. Teammates join through invites, and each has a role and permissions.
- Anyone on the team can assign a task to an agent or a person. Everyone sees the same threads, plans, files and run history.
- Skills are shared across the organization, so one person’s fix helps every agent.
- Agents are shared, and accounts stay personal. A pull request can carry the GitHub identity of the person who directed the work.
- Sharing is the default. Private projects and private tasks keep sensitive work to the people and agents you choose.
- Agents have roles, managers and job descriptions.
- Each task has one owner. An execution lock stops a second run from picking up the same task.
- Tasks link to projects and goals, so each agent gets the reason behind its work.
- Paperclip wakes agents for new tasks, replies and schedules. Routines start work from cron, webhooks or the API.
- A task’s Review and Approval stages keep it open until the reviewer approves it.
- Budgets for the company, each agent and each project pause work at the limit.
- Every run, comment and decision stays in the activity history.
Paperclip runs Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Hermes, OpenClaw and other agents. For coding work, read Paperclip for coding agents.
npx paperclipai@latest onboard --yes
FAQ
How is AI agent orchestration different from workflow automation?
Workflow automation runs fixed steps that you define in advance. Agent orchestration gives work to agents that choose their own steps. It adds ownership, review and cost limits around those choices.
What is multi-agent orchestration?
Multi-agent orchestration is orchestration across more than one agent. Usually the agents have different roles or run on different models.
Do I need orchestration for two or three agents?
Maybe not. If you can watch every session, a good terminal setup is enough. Orchestration starts to pay off when you can’t watch every agent, or when other people assign work to them.
What is multi-player AI?
Multi-player AI means that a team, not one person, works with the agents. Everyone shares the same tasks, context and history, and governance controls who can assign, approve and spend.
Is AI agent orchestration only for coding?
No. Coding is the most common first use, but the same patterns work for support, research, marketing and operations.