# What are AI Agents An AI agent is a configured version of an AI model that has been given a specific purpose, a defined way of behaving, and optionally a set of tools or knowledge it can draw upon to do its job. Where a standard chat conversation is open-ended and general-purpose, an agent is focused. It knows what it is for, how it should respond, and what it has access to. Within GLBNXT Workspace, agents allow your organisation to turn AI into purpose-built assistants that are tailored to specific roles, workflows, or domains, and that any authorised user can access and work with consistently, without needing to configure anything themselves each time. *** ### The Difference Between a Chat and an Agent When you open a standard conversation in Workspace and start typing, you are interacting with an AI model in its default state. It is capable and flexible, but it has no specific context about your organisation, your role, or the task at hand. You provide that context through your prompts. An agent changes this dynamic. It has already been configured with a defined purpose and set of instructions before the conversation begins. A user who opens an agent does not need to explain the context, establish the rules, or set the tone. The agent already knows its job. The user simply starts working. This makes agents particularly valuable in an enterprise setting, where consistency, quality, and efficiency across a team matter as much as the capability of the underlying AI. *** ### What Makes an Agent An agent in GLBNXT Workspace is defined by a combination of the following elements: **A system prompt.** The core instruction set that tells the agent who it is, what it is for, how it should behave, and what constraints it should operate within. This is what gives an agent its character, focus, and boundaries. **A model selection.** The specific AI model the agent uses to generate responses. Different agents within the same Workspace environment can be configured to use different models, matched to the requirements of the task. **Knowledge and documents.** Agents can be connected to specific documents or knowledge collections, allowing them to answer questions grounded in your organisation's own content rather than general AI training data. **Tools and capabilities.** Agents can be equipped with specific capabilities that extend what they can do, such as the ability to search the web, execute code, generate structured outputs, or interact with connected systems. *** ### What Agents are Used For Agents are most valuable when a particular task or workflow is repeated regularly across a team, or when a specific type of AI interaction needs to be consistent, governed, and easy to access for non-technical users. Common examples include: **Role-specific assistants.** A legal assistant configured to review documents for risk and compliance. An HR assistant configured to help employees navigate policy questions. A finance assistant configured to support reporting and analysis tasks. **Domain knowledge assistants.** An agent connected to your organisation's internal documentation, trained to answer questions based on your own policies, procedures, and knowledge base rather than generic information. **Process-specific tools.** An agent designed to support a particular workflow, such as drafting tender documents, reviewing supplier contracts, generating meeting summaries, or producing structured reports in a defined format. **Customer or partner-facing assistants.** Agents that can be deployed to serve external users, with carefully defined scope and behaviour that ensures responses stay within the boundaries your organisation has set. *** ### Agents vs. General Chat Neither approach is better than the other. They serve different purposes and complement each other within Workspace. General chat is best for exploratory, ad hoc, or varied tasks where flexibility matters more than consistency. Agents are best for repeatable, structured, or high-stakes tasks where consistency, quality control, and ease of use across a team are the priority. Many organisations use both. General chat for individual, open-ended work. Agents for the workflows and roles where a purpose-built assistant delivers more reliable results. *** ### Who Can Create and Access Agents Within GLBNXT Workspace, who can create, edit, and access agents is governed by the role-based access control layer. Administrators define which users or teams have the ability to build and publish agents, and which agents are available to which users. End users access agents that have been made available to them by their administrator or by a colleague with the appropriate permissions. From the user's perspective, working with an agent feels the same as working in a standard chat interface. The difference is entirely in the configuration that shapes the interaction from the start. For guidance on building your first agent, see the Building Your First Assistant section. --- # 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/what-are-ai-agents.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.