AI agent orchestration for teams: handoffs that keep context
AI agent orchestration for teams is the handoff across hosts. Shared context keeps the decision, the rejection, the record, and the next step.
Monday the planner lives in ChatGPT. The team picks the smaller launch and kills the platform rewrite. Tuesday a teammate opens Claude and writes the spec from a Slack recap. Wednesday an agent in Cursor starts on the repo. It cannot see the rejection, so it drafts the rewrite. Thursday a reviewer asks why the diff is so large.
Each host did its job. The handoff did not.
People search for AI agent orchestration when that week keeps repeating. Most pages answer with a framework that routes tasks between agents inside one runtime. That answer is useful. It is not the break the team just hit. The break is the handoff between hosts and people. The decision, the ruled-out options, the record, and the next step do not travel.
BitsWeave carries those objects. It does not replace the framework inside one run. It keeps team context where the next host, and the next person, can read it.
What AI agent orchestration means
AI agent orchestration is how you coordinate agents so a job with more than one actor still finishes as one job. Name who acts, what they receive, and who owns the result. The actors can be agents, people, or both. They need the decision, the reason, the ruled-out options, the record, and the next step. The owner is a member, not the chat that just ended.
Inside one program, that is routing. Across products and people, that is a handoff. A team usually has both.
Orchestration inside one runtime
Frameworks orchestrate agents inside one runtime. That is a real job, and they are good at it.
LangGraph models the workflow as a graph: shared state, nodes that do the work, and edges that choose the next node. The graph compiles into one program. State passed to the next node is the handoff, for as long as the process lives.
OpenAI's orchestration and handoffs guide stays inside the Agents SDK. A handoff gives the conversation to a specialist. Agents used as tools stay under a manager that keeps the reply. The next agent receives what that run passes along.
Use this layer when the agents and the loop already share a process. A run that never leaves does not need a second home. The framework answers how agents inside this runtime take turns.
Multi-agent orchestration across a team
Multi-agent orchestration for a team is the other question. The agents do not share a process.
The planner is a person in ChatGPT. The spec is an agent in Claude. The code is an agent in Cursor. The reviewer is a person, sometimes with a second agent in a different host. None of those hosts compiled the others into one graph. There is no shared state object unless the team writes one.
Agentic workflows name the plan. An agentic workflow is multi-step work: plan, call tools, check the result, continue. Orchestration coordinates the actors, including the handoff when the work changes hosts or people. A strong plan inside Claude can still fail when Cursor opens, because the next host never received it.
Host memory is a separate, personal layer. ChatGPT memory versus team knowledge stays inside ChatGPT. Cursor agent memory for teams stays inside Cursor. Neither one is the team's handoff.
| One runtime | Across a team | |
|---|---|---|
| Where the actors are | One graph or one SDK run | ChatGPT, Claude, Cursor, and people |
| What the handoff is | State passed to the next node | Decision, rejections, record, next step, owner |
| Who can read it after exit | The next turn in that runtime | Any connected host, and any teammate |
| What usually breaks | A bad route inside the run | A paste that dropped the rejection |
Both columns can be true in the same week. The research pass can live in one graph. The spec, the code, and the review still change hosts.
Where the handoff fails
The failure is specific. The model can be fine. An object is missing.
The decision stays in the chat that made it. ChatGPT may remember it for that person. Claude does not have the thread, so the spec softens the choice and drops the rejection.
Ruled-out options drop first. People paste the choice and skip what the team killed. The next agent reopens it.
The record and the decision split. The issue says "launch scope." The reason lives in a call. Two agents can share the ticket and still invent two calls.
The next step has no owner. Three bullets and no assignee means the checklist gets written twice.
The reviewer gets the diff only. The constraint that should have kept the change small is not attached. The handoff already failed.
A paste covers one gap for one afternoon. Skip a line, and the next run is confident and wrong. You have become the integration.
What a good handoff carries
A handoff is small on purpose. It is not the transcript. It is the set the next actor can cite.
- Decision. The choice, in one place the org can point at.
- Reason. Why that choice, written with the decision, not left in the call.
- Ruled-out options. The alternatives, marked rejected, so the next agent does not revive them.
- Record. The issue, doc, spec, or pull request the decision rests on.
- Next step. The open work, not a bullet at the bottom of a message.
- Owner. The member who continues. A person or an agent. Not "someone."
Decisions, records, and next steps is the short companion for that set. The owner is what turns a next step into a handoff. A list with no assignee is a suggestion. A task with an assignee is work a member can pick up. Chat logs stay evidence. They are a weak source of truth.
How BitsWeave serves the next host
BitsWeave keeps that set on the organization, so each connected host can read it. It does not route nodes inside LangGraph, and it does not take over an Agents SDK run. Those runtimes keep their jobs. When the work leaves the process, the outcome has a place to land.
A decision keeps the options. You record a title and a problem, then the options. Mark one recommended and the others rejected. Finalizing stamps the selected option and the time. The member who records it is the owner, unless you name another member. The owner can pin it to a project or another entity, so the rejection stays next to the work.
A record grounds the decision. Issues, docs, and app objects land through brokers, or as records the org already keeps. MCP connectors for team context keeps the credential on the org install. Every connected host sees that install.
A next step names an owner. A project holds a tree of tasks. A task has a title, a markdown body, a status, and an assignee. The assignee is a member: a teammate or an agent. Tasks and projects lists the fields. A checklist is a handoff when it cites the decision and names who continues.
One MCP server, then the graph. The connect your agents guide points every client at https://bitsweave.com/api/mcp. Sign-in is OAuth. Claude Code adds the server with one command. Cursor and other JSON-config clients take the same URL. The guide also names the ChatGPT plugin as a host you can install. Each person connects the host they use. They share one active org.
As agents work, the org builds a context graph: activity, ranked identifiers, and relations. Explore context walks /app/context from a focal set, such as a project, an agent, a member, a note, or a decision. Agents call the same reads. context.search finds nodes. context.graph returns the neighborhood. context.hydrate says what a node is. context.query returns evidence, and in answer mode, claims and prose. The org comes from the active org. It is not a call parameter.
Monday then has objects. The rewrite is a rejected option on a finalized decision, pinned to the launch project. The issue is the record. The checklist is an assigned task. Claude writes the spec from the selected option. The agent in Cursor walks from the project to the decision and leaves the rewrite alone. The reviewer opens the same decision.
BitsWeave does not read memory out of ChatGPT, Claude, or Cursor. If nobody writes the decision, the graph does not invent it. If a broker never delivered the pull request, that event is absent. Someone still has to choose, and someone still has to record the options.
A checklist before the next host opens
Use this when more than one host, or more than one person, touches the job.
- Is this one runtime or many hosts? A graph carries state inside a run. ChatGPT, then Claude, then Cursor, needs an object outside that run.
- Is the decision written, with the reason? A choice that lives only in the planning chat gets softened in the next spec.
- Are the ruled-out options on the decision? A missing rejection gets reopened.
- Which record does it cite? Pin the decision to the issue, the doc, or the project.
- Is the next step a task with an owner? Name the member, a person or an agent.
- Can the reviewer read it without the original thread? If the review needs the call, the handoff is not done.
- Do the hosts read the same org? One MCP endpoint is how every host reads it.
Questions teams ask
What is AI agent orchestration?
AI agent orchestration decides who acts next, what they receive, and who owns the result. Inside one runtime, that is often a graph or a specialist handoff. Across a team, it includes the handoff between hosts and people.
Is multi-agent orchestration the same as agentic workflows?
No. An agentic workflow is the multi-step plan: tools, checks, and a next action. Multi-agent orchestration coordinates the agents and the handoffs, including when a different host or a person continues. The words overlap in search. The jobs do not.
Does a framework replace shared team context?
No. A framework routes work inside one runtime. Shared team context holds what must survive after that run, in a host the graph never included. Use the framework for the loop. Use the org for the handoff.
What if the next actor is a person?
A reviewer needs the same decision, rejection, record, and owner. An agent and a person can open that decision. If only other models can read it, the human step is another paste.
Keep the runtime. Move the handoff.
Use a graph or a supervisor when several agents share one runtime. That is AI agent orchestration for a process. Use a shared org when the planner, the spec, the code, and the review live in different hosts, with different people.
Write the decision, including what you ruled out. Pin the record. Assign the next step. Let the next host read that neighborhood.
Connect ChatGPT, Claude, or Cursor at bitsweave.com. The runtime can change. The handoff should stay.