You settle a product question in one AI conversation, open a coding agent to implement it and spend the next few minutes explaining the project again. The specification survives the move. The reason behind it, the rejected alternative and the latest correction often do not.
To share project context across AI tools, keep a maintained project record outside any individual conversation. Give each authorized tool access to the relevant sources, save important decisions with their evidence and ask the next agent to check the current record before continuing. A shared document can be enough for a small task. Work involving several people, agents and decisions benefits from a workspace that preserves their contributions together.
This is a practical guide to setting up that workflow with Swarm. The same principles apply if your team begins with an ordinary document. The test is simple: can a second agent continue the work accurately without the person reconstructing it?
Start with a decision the next tool needs
Consider a software team adding filters to its project search. A product manager explores the behavior in Claude, a developer works with a coding agent and a designer uses ChatGPT to review the wording.
During planning, the team decides that the first release will filter by status. Filtering by owner remains a proposal. Archived projects stay excluded because the existing search does not include them. Keyboard navigation must continue to work.
Those details are easy to lose in a summary such as “add the new search filters.” An incoming agent might implement both proposed filters, include archived projects or treat the discussion as permission to release the change.
The useful shared context is compact:
| Record | What it establishes |
|---|---|
| Current brief | Add a status filter to project search |
| Accepted scope decision | Owner filtering is deferred; archived projects remain excluded |
| Relevant source | Current search behavior and the approved interface requirements |
| Open question | How the active filter is announced to keyboard and screen reader users |
| Next contribution | Prepare an implementation plan and identify checks; do not release changes |
Try this with a small, nonconfidential project before connecting production systems.
Decide what belongs outside the conversation
An AI conversation contains exploration as well as decisions. Saving the entire transcript does not tell the next participant which parts still apply.
Preserve the objective, accepted constraints, evidence that changes the answer, unresolved questions and the next responsibility. Keep source links so someone can inspect the basis for a conclusion. A useful record explains why archived projects are excluded, not simply that an earlier assistant said so.
Leave room for changes. If the team later accepts owner filtering, add the new decision and identify the earlier scope it replaces. An agent should be able to distinguish current instructions from a reasonable decision made before the requirements changed.
Avoid saving private credentials, unrelated conversations or sensitive customer details simply because they were present during the work. Shared context should have an intentional audience. The people and tools contributing to it may have different access.
For work that stays with one person in one tool, a short project note may be entirely adequate. The coordination problem becomes more significant when someone else needs to use the record, correct it or act on it in a different environment.
Create one private Space for the project
In Swarm, create a private Space around the outcome, such as “Project search filters.” Add the brief and the small set of sources needed to understand the work. Give the participating people appropriate access before inviting their agents to contribute.
Keep the starting assignment narrow. The Space does not need every company document or every earlier chat. It needs enough information for the next participant to make a useful contribution without guessing.
The Spaces guide explains creating a Space, adding members, selecting active context and saving records. Active context lets you select material for the current work; it is not a promise that every stored source will fit into every model request.
A good first brief names the result and its boundary in plain language: prepare the status filter plan, preserve the accepted exclusions and flag missing evidence. This gives both the agent and the reviewer a clear basis for judging the next output.
Connect each tool through its supported route
MCP is a common way for an AI application to access external tools and context. The MCP architecture guide describes the host, client and server relationship. The protocol supplies a connection; the application still decides how to use the material it receives.
Swarm's connection guide provides the current setup for compatible clients. Start there to obtain the correct connection details, authorize the intended Space and choose the access required for the work. Keep credentials in the client's supported authentication flow, outside messages and shared notes.
If you use ChatGPT, OpenAI's custom MCP server documentation explains adding a server and managing its tools. Claude's remote connector guide describes its setup. Workspace policies and available client features can affect whether either route is available to you.
A coding agent needs its own supported connection too. Do not assume that connecting one chat application gives every other application access. Confirm which account, Space and capabilities each connection can use.
Start by asking the agent to read the selected Space and explain the current objective. If it cannot retrieve the record, resolve the connection or access issue before asking it to continue from memory. Avoid broadening permissions simply to remove an error.
Save what the first session actually established
After the planning discussion, ask the first agent to prepare a handoff. It should name the accepted scope, link the evidence it read, preserve the open accessibility question and identify the next contribution.
You can begin with this request:
Prepare a handoff for the project search filter work in this Space. Separate accepted decisions from proposals. Include the relevant source references, unresolved questions and the next permitted step. Show me the draft before saving it. Do not change the repository or release anything.
Inspect the draft. Does it preserve the status only scope? Does it treat owner filtering as deferred? Does it leave the accessibility question open? Correct any mistakes before asking the agent to save it.
Swarm's handoff flow can preview a checkpoint before publication. A preview is still a draft. Confirm the saved record and its sources after publication so the next session has something durable to retrieve.
The record should describe completed work honestly. A proposed plan is not an implemented feature, a suggested test is not a passing test and a saved handoff does not authorize a release.
Continue in a fresh conversation
Open a new conversation in the second connected tool. Give it the project identity and the question it needs to answer, without pasting the entire previous conversation.
Read the current record in the Project search filters Space. Explain the accepted scope and the unresolved accessibility question, with source references. Then propose the next implementation checks. Do not edit files or release changes.
Compare its answer with the actual record. It should retain the exclusions, recognize the deferred proposal and identify what remains undecided. Follow the cited sources yourself, especially if the answer claims that an earlier decision changed.
If the response is wrong, inspect the failure before adding more context. The agent may have read an outdated source, missed the saved checkpoint, encountered an access restriction or interpreted a proposal as an instruction. Each problem needs a different correction.
The new agent can then save its own contribution to the same Space. For example, it might prepare an implementation checklist that the designer reviews from another tool. The participants are building on a shared project record while retaining their own conversations and working environments.
Our agent handoff guide offers a more formal exercise for comparing what an incoming agent can recover.
Shared context does not mean identical answers
Two models can inspect the same sources and reach different conclusions. They may use different context budgets, instructions or tools. Shared access makes the disagreement inspectable; it does not make the models interchangeable.
A Space also does not transfer another tool's private working state, automatically expose its local files or keep a closed conversation running. If a coding agent needs a repository, its host needs authorized repository access. If work must continue unattended, it needs an eligible running executor or a deliberately configured hosted route.
Be especially careful with decisions that authorize action. The second agent can read that a person approved a particular plan. It still needs the appropriate permission for its next operation. A general project summary should never become permission to send messages, modify production or spend money.
For a deeper explanation of selecting evidence for a particular task, see our guide to context engineering.
Keep the record useful as the project changes
Ask contributors to leave a small, reviewable update after meaningful work. Include what changed, which sources support it and what the next participant needs to know. Avoid repeatedly publishing summaries that add no new information.
When a requirement changes, update the shared record before starting another assignment that depends on it. When an output is corrected, preserve enough history to explain the correction. Identify a person responsible for resolving competing decisions.
You can evaluate the workflow without complicated measurement. Notice when a new participant asks for information that was already available, continues a superseded plan or cannot locate the evidence behind a conclusion. These are useful signals about the record and the handoff.
Keep a distinction between a retrieval problem and a judgment problem. If the correct requirement was missing, repair the context. If it was available but misapplied, inspect the instructions and review the agent's work. More stored material will not solve every failure.
Try it with one project and two tools
Choose work that already moves between two AI tools. Create a private Swarm Space, save the brief and one important decision, then connect the first tool through the connection guide.
Have it prepare a handoff, review and save that record, then ask a fresh session in the second tool to recover the decision and propose the next step. Keep external actions unavailable during this first exercise.
The useful result is modest and concrete: the next participant understands the work, can inspect its evidence and knows where to continue. Once that works reliably, the same Space can support a larger project without requiring everyone to work in the same chat window.