AI Agent Orchestration: How to Run Several AIs Together

AI agent orchestration is how you get several AI agents to work on one job without stepping on each other: who does which part, how work moves between them, what they remember, and who signs off. For most people that means running Claude, ChatGPT, Codex, Cursor, or Grok together, with a clear handoff and a person approving anything that matters.

Search for this topic and you mostly find enterprise platforms and developer frameworks for building multi-agent apps. Those are real, but they answer a different question. This guide is for the person who already pays for two or three AI tools and is tired of being the copy-paste layer between them. It is also the hub for everything else on this blog, so each section links to a deeper article.

What is AI agent orchestration?

An AI agent is a model that can take actions: read files, run commands, call tools, browse, or send work somewhere. Orchestration is the layer above the agents. It decides:

Who does what: which agent gets which part of the job.
How work moves: what gets passed from one agent to the next, and in what form.
What they know: which rules and memory each agent can see.
When it is done: what evidence counts, and which results need a human yes.

In a developer framework, code does that orchestration. In most people's lives, a person does it by hand, across browser tabs. I wrote about that in The Human API Problem.

Why use more than one AI agent?

Because the tools are good at different things, and each one has limits.

Different strengths: one agent might be your favorite for writing code, another for research, another for reviewing.
Usage limits: every subscription caps usage in some window. A second tool keeps work moving when the first one is out.
A second opinion: a reviewer agent that did not write the code catches mistakes the author agent explains away.
Different surfaces: some agents live in your terminal, some in an editor, some in a phone app, some in a chat with no integrations.

The catch is coordination. Every agent you add is another place where context, decisions, and "done" can get lost.

What are the common multi-agent patterns?

Most setups are one of four shapes, or a mix:

Builder and reviewer: one agent does the work, another checks it. Simple and effective.
Relay: work passes down a line. Research, then draft, then edit, then check.
Router: a lead agent reads the request and sends each piece to the agent best suited for it.
Fan-out: several agents tackle parts of a job in parallel, and one pulls the results together. Coding agents now do this inside a single product: Claude Code can run parallel agents, and Codex supports subagents.

Whatever the shape, each handoff needs the same three things: a short brief, the evidence that would prove the work is done, and a flag for whether a person has to approve the result.

Which AI agents should you combine?

That depends on where you work and what you already pay for. The comparison guides go deep on each matchup, with prices checked on the vendors' own pages:

Codex vs Claude Code: OpenAI's and Anthropic's coding agents, and how to run them on the same repository.
Cursor vs Claude Code: an AI editor with model choice versus a Claude-first agent, and how to use them together.

A common starting pair is one coding agent plus one general chat assistant. Add a third only when you can name the job it will own.

How do AI agents connect to tools and to each other?

Two layers matter here.

Agent to tool: the Model Context Protocol, MCP, is an open standard for connecting AI apps to tools and data. Claude, ChatGPT, VS Code, and Cursor all support it. MCP vs API explains when to use each.
Agent to agent: there is no shared standard most consumer tools use for handing work to each other. In practice, people copy and paste. That is the gap.

A good handoff is short. Pass the goal, what changed, and what proof you expect. Do not pass a whole transcript. The next agent pays to read every token of it, which AI token cost explains with real numbers.

How do you keep a human in control of AI agents?

Decide in advance which actions need your yes. Publishing, spending money, sending messages as you, merging code, and deleting things are the usual list. Everything else, the agents can do freely.

Two rules make that work. First, ask for evidence, not a summary: the URL, the test output, the diff. Second, keep approvals in one place, so you actually read them. Human in the loop AI covers what to gate and how to avoid rubber-stamping, and Done Is Not Done Until It Is Verified explains why evidence comes first.

How do multiple AI agents share memory?

Mostly, they do not. Claude, ChatGPT, Codex, and Cursor each keep their own memory or instruction files. Some overlap exists for coding: Codex reads AGENTS.md, and Claude Code can read the same file. For everything else, you repeat yourself in every tool.

A cleaner model is to split memory by area of life, like work and family, rather than by vendor, and to approve what gets remembered. Claude memory vs ChatGPT memory covers how today's assistants handle it, and What Is a Brain? covers the area-of-life idea.

What does it cost to run several AI agents?

Each tool bills separately. Subscriptions run from free tiers to $200 a month or more, and API usage is billed per token. Agents are expensive mostly because they loop, re-reading the same context every turn. Short handoffs, prompt caching, batch processing, and the right model size for each job keep the bill down. AI token cost has the current per-token prices and worked examples.

How do you start orchestrating AI agents?

A setup you can try this week:

Pick two agents and give each a clear job, such as builder and reviewer.
Write one rules file both can read, with what they may and may not do.
Use a handoff template: goal, context, expected evidence, and whether it needs your approval.
Gate the risky actions in each tool's settings.
Keep a log of what you approved and why.

When that starts to feel like a part-time job, you have found the real problem.

Where does iBrains fit?

I am building iBrains to do that coordination job. It is not another model, and it does not replace the AIs you pay for. It gives them a shared way to hand work through the brain: a handoff packet with a brief, the evidence you expect, and an approval flag. Agents receive packets through a signed webhook, an inbox they check over MCP or REST, or a signed link you carry into a chat with no connector. A result with no evidence is refused, and gated results wait in one approval queue per brain. iBrains is in private development and invite-only right now. How it works walks through the flow, and the connection docs show how an agent plugs in.

Frequently asked questions

What is the difference between AI orchestration and AI agent orchestration?

AI orchestration is the broad idea of coordinating AI models, data, and tools in a workflow. AI agent orchestration is the narrower case where the parts are agents that take actions, so the hard problems are handoffs, permissions, and checking results.

What is multi-agent orchestration?

Multi-agent orchestration means coordinating two or more AI agents on the same job. Common patterns are builder and reviewer, a relay from one stage to the next, a router that assigns work, and parallel agents whose results are combined.

Do I need to code to orchestrate AI agents?

No. You can orchestrate agents with clear roles, a shared rules file, a written handoff brief, and approval settings in each tool. Code and frameworks help when you build an app with agents inside it.

Can Claude and ChatGPT work together?

Not directly. They do not share memory or a task list. You can still use them together by giving each a role and passing a short brief between them. Both support MCP, so they can connect to the same tools.

What is the biggest risk with multiple AI agents?

Trusting a claim of "done" that nobody checked. More agents produce more output and more confident summaries. Require evidence for every result and a human yes for anything that publishes, spends, merges, or deletes.