# Sharing agents across teams Building a well-configured assistant is most valuable when it can be used by everyone who needs it. GLBNXT Workspace allows agents to be shared across individuals, teams, and the broader organisation, turning a single well-built configuration into a resource that delivers consistent value at scale. *** ### Why Sharing Agents Matters When an agent is shared, every user who has access to it benefits from the work that went into building it. They do not need to understand how to configure an AI model, write a system prompt, or attach the right knowledge. They simply open the assistant and start working. This is where agents move from being a personal productivity tool to an organisational capability. A legal team that builds a strong contract review assistant and shares it across the department multiplies its value immediately. A compliance team that publishes a regulatory guidance assistant removes the need for every individual to craft their own prompts for the same recurring task. Over time, a shared library of well-built agents becomes one of the most tangible expressions of AI maturity within an organisation. *** ### How Sharing Works Agents can be shared at different levels of scope depending on your permissions and the needs of the intended audience. **Individual sharing.** An agent can be shared directly with a specific colleague, giving that person access without making it available to anyone else. This is useful for handovers, collaboration between individuals, or giving a specific user access to a tool built for their role. **Team or group sharing.** Agents can be shared with a defined team or group within your organisation. Everyone in that group gains access simultaneously, making it straightforward to roll out a new assistant to an entire department or project team at once. **Organisation-wide sharing.** Agents can be published to the broader organisation, making them discoverable and accessible to all users within your Workspace environment. This is appropriate for assistants with broad applicability, such as a general writing assistant, a company policy guide, or a tool relevant to all employees regardless of role. The sharing options available to you depend on the permissions your administrator has configured for your account. *** ### The Agent Library As more agents are built and shared within your organisation, they become part of a collective agent library that users can browse and access from within the Workspace interface. Agents in the library are displayed with their name and description, allowing users to quickly identify which assistant is relevant to their task. This is why the name and description you give an agent at the point of creation matter. A clear, purposeful name and a concise description make an agent discoverable and immediately understandable to a colleague who encounters it for the first time. Agents without clear descriptions are rarely used by anyone other than the person who built them. *** ### Access Control and Permissions Sharing an agent does not mean giving users the ability to edit or reconfigure it. Access to an agent and the ability to modify it are governed separately through the role-based access control layer. By default, users who have access to a shared agent can use it in conversations but cannot change its system prompt, model configuration, knowledge attachments, or tool settings. Only users with the appropriate editing permissions can modify an agent's configuration. This separation between use and edit access is important for maintaining the integrity of shared agents. It ensures that a carefully configured assistant remains consistent for all users, regardless of who is using it or how frequently. Administrators can define and adjust these permission levels at the organisation, team, or individual level. *** ### Updating a Shared Agent When an agent is updated by someone with editing permissions, the changes take effect for all users who have access to it immediately. There is no need to re-share the agent or notify users manually. The next time a user opens the assistant, they are working with the updated configuration. This makes it straightforward to improve and iterate on shared agents over time based on user feedback, without disrupting access or requiring any action from end users. When making significant changes to a widely used agent, it is good practice to communicate the update to your team so users are aware of any changes in behaviour or scope. *** ### Retiring and Removing Agents Agents that are no longer needed can be unpublished or deleted by a user with the appropriate permissions. Unpublishing removes the agent from the shared library without deleting it permanently, which is useful if you want to take an agent offline temporarily while reconfiguring it. Deleting an agent removes it entirely and cannot be undone. Before retiring a shared agent that is in active use, it is good practice to notify the users who rely on it so they have time to find an alternative or transition their workflow. *** ### Tips for Managing a Shared Agent Library **Establish clear naming conventions early.** As the number of shared agents grows, a consistent naming approach makes the library much easier to navigate. A convention that includes the team, purpose, and scope, such as "Legal: Contract Review" or "HR: Policy Q\&A", scales well as the library expands. **Assign ownership.** Every shared agent should have a clear owner who is responsible for maintaining and iterating on it over time. Without ownership, agents become stale and lose effectiveness as the workflows they support evolve. **Review agents periodically.** Scheduled reviews of your shared agent library help identify assistants that are underperforming, outdated, or no longer needed. A smaller library of high-quality, well-maintained agents is more valuable than a large library full of inconsistent or abandoned configurations. **Gather user feedback actively.** The people using a shared agent every day will quickly develop a clear sense of where it works well and where it falls short. Building a lightweight feedback loop, even informally, accelerates improvement and keeps shared agents aligned with real user needs. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.glbnxt.com/workspace/agents-and-assistants/sharing-agents-across-teams.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.