How to Share an AI Assistant With Your Team

To share an AI assistant with your team, share its instructions and approved knowledge using the app’s supported member access, while each person signs in through their own account. Decide separately who can use the assistant, edit its setup, and access its sources. Sharing a password does not give you those controls.

If teammates use different AI apps, fenbs can hold the project’s shared decisions, tasks, and current context for their connected assistants. First decide what you are sharing: the assistant’s behavior, the material it may read, or the work it needs to continue.

What are the steps to share it safely?

1. Choose one job for the shared assistant, such as drafting internal project updates from approved records.
2. Write the instructions and select sources that every intended member is allowed to use.
3. Use the app’s supported workspace or project sharing controls. Invite people under their own accounts and choose the appropriate permissions.
4. Test from a teammate’s account, including a question the assistant should answer and one it should decline or escalate.
5. Name an owner for updates and access changes, then agree what participants must save after using it.

The exact sharing controls depend on the product and account. For example, Anthropic’s project sharing instructions↗ (opens in a new tab) describe member invitations with different viewing and editing permissions, subject to organization settings. Follow the current vendor guide rather than assuming that a link carries the same access in every app.

A shared assistant for internal updates needs a narrower setup than an assistant allowed to change client work. Start with a small job whose output a teammate can check. Broaden access only when the shared record and review process support the next responsibility.

What belongs in the assistant’s shared configuration?

Configuration is the reusable instruction that tells the assistant how to approach its job. Include its purpose, intended audience, allowed sources, output format, and what requires a person’s approval. Keep private preferences and unrelated personal information out of the team version.

For a project update assistant, the instruction might say: “Summarize the current tasks and recorded decisions for internal colleagues. Separate completed work from unverified work and questions waiting on a person. Do not describe a suggestion as an approval. If the source record is incomplete, identify the missing information.”

Keep the instruction specific enough that two people can test the same behavior. “Be helpful and know our company” gives neither person a reliable way to recognize a failure. A clear output and a clear approval boundary make review easier.

If you only need to pass reusable wording to a colleague, use the narrower guide to sharing AI prompts. Sharing an assistant also includes its sources, permissions, and ongoing maintenance.

How do you share approved knowledge?

Make a small source set for the selected job. For an internal project update, that might be the project goal, current tasks, approved decisions, and remaining checks. A company handbook may be useful background, but it does not tell the assistant what happened on this particular project.

• Identify which record is authoritative and who maintains it.
• Check that each intended person and the selected AI app may access the information.
• Remove personal conversations, credentials, and material unrelated to the shared job.
• Keep references to source material so a reviewer can check an important claim.
• Write down unresolved questions instead of letting the assistant fill them with assumptions.

Do not assume sharing a configuration also shares its connected sources, or that granting source access also grants permission to edit it. Test these as separate questions. If the app cannot enforce the access boundary you need, use a smaller source set or choose a different workflow.

The guide to an AI brain explains why a reusable work record matters. In fenbs, that record can stay alongside the project’s tasks and decisions; see how the context is organized.

How should a teammate test the shared assistant?

Use a teammate’s ordinary account instead of demonstrating from the creator’s account. The creator may have wider document access or private context that makes the example work. Ask the teammate to open the shared setup and answer a few realistic questions without coaching.

Use fictional restricted material for the last check. You can detect an access mistake without exposing something sensitive to prove the mistake exists. If the assistant fails, correct the configuration, source selection, or permissions and repeat the affected check.

For practical habits around reviewing and using the output, read how to use AI at work. A working invitation is the beginning of sharing, not evidence that the assistant is ready for every task.

What if your team uses different AI apps?

Share the underlying work record and adapt the instructions to each supported app. The goal is for each assistant to start with the same approved decisions and current state. You do not need identical wording from every model, but you do need agreement about what is authorized and what remains uncertain.

For example, a developer may use a coding assistant while a designer uses a general chat assistant. Both need to know that a simpler form was approved and that its delivery test is unfinished. The guide to Cursor covers that coding tool separately; sharing context does not make every assistant the same kind of tool.

fenbs keeps the work outside one AI app so connected assistants can read the same project record; the shared context workflow depends on asking each assistant to read before working and save progress before it stops.

How to do this with fenbs

1. Create a free account and try the project context on your own personal kanban board. It is free for one person, with no credit card; use a team kanban board when colleagues need shared access.
2. Add the project and its current tasks. Record the approved decisions and rules, then write where work stands and what the next session should do.
3. Give the team’s members roles appropriate to their work. Keep questions requiring a person’s decision open rather than turning an assistant’s proposal into approval.
4. Follow the guide for each supported AI app on Integrations. MCP is a standard way for an AI app to use an outside tool, and the connection uses a browser sign-in.
5. Ask a teammate’s connected assistant to read context and explain the next step. Check its answer, then ask it to save progress after the test or the next real task.

fenbs does not share your AI app account or import your chat history. If all you need is a reusable assistant inside one app, that app’s native sharing may be sufficient. fenbs fits when the project’s decisions and progress need to remain available across supported assistants.

Who maintains access and updates afterward?

Name an owner who can review instructions, replace stale sources, and remove access when a teammate leaves the work. Check the AI app, the source documents, and any outside tool connections separately. Removing a member in one place may leave access elsewhere.

Protect each individual login using the options the provider offers. The FTC recommends two-factor authentication↗ (opens in a new tab) as an additional account protection. Keep recovery codes and login credentials out of shared instructions and project records.

Review the shared setup when the project’s rules change, not just when someone reports a bad answer. Keep the original acceptance questions and rerun the affected ones. Find more ways to maintain the shared record in the team brain library.