AI Context Transfer: Move Work to Another Assistant

An AI context port is a deliberate transfer of the information another assistant needs to continue a task: its goal, sources, approved choices and remaining checks. You usually do it with a reviewed handoff and accessible files, rather than trying to move a model’s internal state. The test is whether the next assistant can take the right next action.

A shared work record such as fenbs makes that handoff repeatable: supported connected assistants read the same project context instead of relying on separate copies of an old chat. You can start with the steps below even if you use only a document today.

How do you transfer context in practice?

1. Pause at a clear stopping point and name the specific task being handed over.
2. Gather the current deliverable, controlling sources and decisions a person approved.
3. Write what was completed, what was actually checked and what remains uncertain.
4. Give the next assistant access to those materials and ask it to explain the next action.
5. Correct misunderstandings, then authorize a small continuation and review the result.

For example, imagine a designer handing a draft specification to a coding assistant. “Build the latest design” leaves several questions unanswered. “Implement the approved mobile navigation in this file; the desktop layout is still a proposal” identifies both the work and the boundary around it.

What belongs in the handoff packet?

Include information because it affects the next decision. A long chronology can contain the answer while making it difficult to find. Put the current instruction first, then the evidence needed to act on it. Link older discussion only when it explains an unresolved question or a choice whose reason matters.

Add a timestamp or version when the material can change. “Reviewed against the attached draft” is more useful than “reviewed,” because it tells the next assistant what the check actually covered. In fenbs, this distinction belongs with the task and Where we left off, so the summary points back to the work behind it.

How much conversation should you include?

Start with the smallest packet that explains the next action, then provide supporting material on demand. The context window is the information a model can use while generating its answer. Anthropic’s context window documentation↗ (opens in a new tab) explains why curating that information matters; a larger allowance does not make every extra page useful.

Keep an original source available when a summary removes a distinction the task depends on. If you are checking contract wording, include the relevant exact clause rather than only a summary of its meaning. If you are fixing a bug, include the failure and affected version rather than only “the checkout failed.”

The context window guide explains capacity, while AI context rot covers declining usefulness in crowded sessions; transfer is about choosing what another session needs to begin. For shortening a record while retaining its meaning, see AI context compression.

How can you preserve decisions without promoting guesses?

Label each important statement by its status. A person’s approved choice is different from an assistant’s suggestion, and a successful check is different from a proposed check. Those distinctions are easy to lose when a summary turns a whole discussion into confident prose.

• Approved: the project owner chose the shorter form for the first release.
• Observed: the form opens on a phone in the checked browser.
• Not checked: delivery of the submitted message has not been tested.
• Proposed: remove the optional address field; a person still needs to decide.
• Next: test message delivery before describing the form as complete.

Read the finished handoff yourself and compare each approval or test claim with its source. If no evidence supports a claim, mark it as uncertain. fenbs keeps people’s choices in Decisions and rules and records changes in History; the work record overview explains how those parts sit together.

What if the next assistant cannot open a source?

A visible link does not prove access. Ask the assistant to identify which materials it actually read and which it could not reach. Supply a permitted copy or a relevant excerpt for the missing material, and say which original it came from. Do not let a missing attachment quietly become a claim that the document contained nothing relevant.

Tool instructions also need a supported home. For example, OpenAI documents how Codex reads AGENTS.md instructions↗ (opens in a new tab). Other apps have their own mechanisms, so check the destination rather than expecting a filename to work everywhere; the context files comparison provides that separate reference.

Keep source material distinct from instructions about how to act. A quoted message inside a research file is evidence to evaluate, not permission to send messages or change accounts. State the action limits in your handoff, especially when the destination can use tools.

How to do this with fenbs

1. Create a free account, free for one person with no credit card, and add the project.
2. Add the current task, its plan and the evidence from checks already performed.
3. Record people’s approved choices in Decisions and rules and add useful facts as AI context notes.
4. Save Where we left off with the next action, blockers and anything waiting on a person.
5. Connect a supported assistant from Integrations over MCP, a standard way for AI apps to use outside tools, and ask it to read the context before continuing.

fenbs does not move a model’s hidden state or read your chat history. It is not the right fit if you need only a one-time transcript attachment. It helps when work will continue across sessions and someone needs to keep decisions, checks and next steps current.

How do you know the transfer worked?

Before new work begins, ask the assistant for the current goal, the decision it must preserve and the first unresolved check. Its answer should agree with the record and identify missing evidence. Then review a small piece of continued work, such as a proposed edit, before allowing broader changes.

After that continuation, save a new stopping point. The original transfer describes a moment in time; it should not remain the active summary after the task changes. Browse the switching models hub for destination-specific details while keeping this read, verify, work and save loop consistent.