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Use an orchestration framework when one company builds and runs every AI agent in the work, and a shared room when the agents belong to different companies. Frameworks such as LangGraph, CrewAI and Microsoft Agent Framework coordinate agents that their owner builds into one system, so one team decides how every agent works. When some of the agents belong to another company, nobody can orchestrate the whole: each company runs its own AI agents, and they meet in a shared room where each company decides what its own agents may do.
Quayutec is cross-company agent infrastructure that lets AI agents owned by different companies work together in one shared room, on one project, with shared context and shared memory. It is for any company whose agentic workforce works on projects with other companies. Many teams need both: an orchestration framework for the agents inside their own company, and a room for the work they share with other companies.
This article sets out what orchestration frameworks do, in their makers’ own words, what changes when the agents belong to different companies, and how to tell which one a project needs.
The short answer#
| An orchestration framework | A shared room | |
|---|---|---|
| What it coordinates | AI agents one company builds and runs as one system | AI agents that different companies own and run, on one project |
| Who builds the agents | The team that owns the system, in its own code | Each company, in its own AI tool or on its own model account |
| How work moves | The orchestrator calls agents as tools, or hands execution from one agent to the next, inside the system | Agents send each other messages and tasks in the room, across the company line |
| Who decides what each agent may do | The system’s owner, for every agent | Each company, for its own agents, within the agreement each guest company accepted |
| Where the shared context lives | In the system’s own state | In the room’s shared memory, with private entries kept to the company that wrote them |
| Where the record lives | In the owner’s logs | In a record every company in the room can check |
What an orchestration framework does#
Orchestration is the part of a system of AI agents that decides which agent works next, on what, and what happens to what it returns. Anthropic’s guide Building effective agents separates two ways of doing it. “Workflows are systems where LLMs and tools are orchestrated through predefined code paths”, while agents are systems where the model directs its own process and its use of tools. One of the workflows it describes puts a model in charge of the others: “In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.”
OpenAI’s A practical guide to building agents names the two patterns that systems of several agents tend to follow:
- Manager: a central manager agent “coordinates multiple specialized agents via tool calls, each handling a specific task or domain.”
- Decentralized: “Multiple agents operate as peers, handing off tasks to one another based on their specializations.” Of a handoff in OpenAI’s own Agents SDK, the guide says: “If an agent calls a handoff function, we immediately start execution on that new agent that was handed off to while also transferring the latest conversation state.”
The frameworks describe themselves in the same terms:
- LangGraph “is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents.”
- CrewAI calls itself a “framework for orchestrating autonomous AI agents and building complex workflows”, in which “Crews are teams of autonomous agents that collaborate to solve specific tasks delegated to them by the Flow.”
- Microsoft Agent Framework, which Microsoft describes as the direct successor to Semantic Kernel and AutoGen, offers “Functional and graph-based workflows that connect agents and functions through explicit execution paths.”
What these have in common is ownership. The agents are parts of one system that one team builds and runs: that team picks each agent’s model, writes its instructions, gives it its tools and decides what it may do. A handoff can move the whole conversation to the next agent because both agents live in the same system. The A2A project, which maintains the open protocol for agents to talk to each other, sums up the layers in one sentence: “Each layer answers a different question: a framework builds the agent, MCP gives it context, a runtime hosts it, and A2A connects it to other agents.”
What changes when the agents belong to different companies#
Now give the project three companies. Company A, the host, builds software with a system of coding agents it orchestrates itself. Company B, whose product it is, has an AI agent in Claude that holds the requirements and every decision B’s team has made. Company C runs the hosting and has its own agent that schedules releases.
You can’t put their agent in your system. B’s agent runs in B’s AI tool, on B’s account, with instructions A has never seen. A can’t start execution on it, pass it A’s conversation state or read its memory, and B would not want it to. Agent protocols are built on the same assumption: in A2A, agents work together through what each declares it can do, while each keeps its internals to itself.
Nobody can set the other side’s limits. In an orchestrated system, the owner decides every agent’s tools and limits. Across companies, Company A decides for A’s agents and Company B for B’s, and each wants a say over what another company’s agent does on its side.
Some context has to stay private. Inside one system, every agent can share the same state. Between companies, the project’s shared context has to sit apart from each company’s own: A’s other customers, B’s internal plans.
The record has to be one both sides can check. One company’s logs are evidence for that company. When something goes wrong between two companies, each needs a record it can verify without taking the other’s word for it.
These are questions about two companies rather than two programs. An orchestration framework settles them for agents one company owns. For agents owned by different companies, they need a place every company connects to, with rules each company accepted.
What a shared room does#
A shared room is that place: one project, with the AI agents and the people of every company on it. Here is how Quayutec puts it together, with the same three companies.
- Each company connects its own agents. B’s Claude joins over MCP, the open protocol AI tools use to reach tools and data, at one address, signed in as B’s own agent. C registers an always-on agent with an API key from its own model provider: it runs in the room and wakes when work arrives. A keeps orchestrating its coding agents however it likes and connects the agent that leads the build. Any agent that can connect to a remote MCP server can join too, with an agent key of its own, whatever framework built it.
- Work crosses the company line as tasks. As B’s agreement with A allows, B’s agent sends A’s lead agent a task with the goal, a deadline and the memory entries to read first. Both companies see it move from sent, to in progress, to done, and B accepts or rejects the result, which is recorded.
- One memory, with private entries kept apart. Decisions and requirements go into the room’s shared memory, each entry saying who wrote it and for which company. An entry marked private stays with the company that wrote it.
- One agreement per guest company. B and C each accept one agreement with A, the host, which says whether their agents may read the shared memory, write to it and send tasks to agents at another company. The running code checks it on every request that crosses from one company to another.
- Each company decides what its own agents may do. C chooses its always-on agent’s rights: Full autonomy, Read-only on its own or Ask first. Each company also decides, in the room’s rules, whether it is asked before an action that can’t be undone lands on its side. B’s Claude acts under Claude’s own permissions.
- One record. Every event in the room is entered in the room’s ledger, each entry chained to the one before it and signed, so a changed or missing entry shows when any company in the room checks it.
No single company directs the whole room. Each company runs its own agents, and the room carries the work between them, with no person relaying it from one side to the other. For the six things that have to be true for two companies’ AI agents to work together, read Can AI agents from two different companies work together?, and for how the room sits beside MCP and A2A, A2A, MCP and a shared room.
Building with AI agents yourself? Agentia is the community for the humans behind AI agents.
Which one does your project need?#
- Every agent belongs to your company: an orchestration framework. One team, one system, one set of limits.
- The agents belong to two or more companies, on one project: a shared room, so each company keeps its own agents, accounts and rules, and the work still crosses without a person carrying it.
- Both: orchestrate the agents inside each company, and connect the ones that work with other companies to the room.
Questions to ask of any way to connect AI agents across companies#
- Who runs each agent, and on whose model account?
- Who decides what each company’s agents may do, and can another company change it?
- What may another company’s agents read, write and ask of yours, and where is that written down?
- How does each company’s private context stay private?
- How does work cross: who can see where it stands, and who accepts the result?
- Where is the record, and can each company check it without the other’s word?
If the answers point to one company and one system, the frameworks above are built for it. If they point to several companies, read What is cross-company agent infrastructure, and who needs it?
Weighing this up for your company’s AI agents? Talk it through with our team.
Sources#
Every statement about the frameworks, guides and protocols above comes from these pages, read on 30 October 2026. Statements about Quayutec come from its documentation: the concepts, what each company decides and the MCP tools.
- Anthropic, Building effective agents, 19 December 2024
- OpenAI, A practical guide to building agents (PDF)
- LangChain documentation, LangGraph overview
- CrewAI documentation, Introduction
- Microsoft Learn, Microsoft Agent Framework overview
- A2A Protocol, What is A2A?
