# Building your first agent Creating an AI assistant in GLBNXT Workspace does not require any technical background. The agent builder is a guided interface that walks you through the configuration step by step. Within a few minutes, you can have a purpose-built assistant ready to use and share with your team. This section walks you through the process from start to finish. *** ### Before You Start Before opening the agent builder, it helps to have a clear idea of what your assistant is for. The most effective agents are built around a specific, well-defined purpose. A few questions worth thinking through before you begin: * What task or workflow is this assistant designed to support? * Who will use it, and what do they need it to do? * What tone, style, or behaviour should it have? * Does it need access to specific documents or knowledge? * Are there things it should explicitly not do or say? You do not need detailed answers to all of these before starting. But having a clear sense of the assistant's purpose will make every configuration decision easier, and will produce a more effective result. *** ### Opening the Agent Builder The agent builder is accessible from within the chat interface. Look for the agents section in the sidebar or the model selector, depending on your interface configuration. Selecting the option to create a new agent opens the builder panel, where you will configure your assistant. If the option to create agents is not visible in your interface, it may not be enabled for your account. Contact your administrator to request access. *** ### Step 1: Give Your Assistant a Name and Description Start by giving your assistant a clear, descriptive name that communicates its purpose to anyone who encounters it. Avoid generic names. An assistant called "Contract Review Assistant" is immediately understood by a colleague who has never seen it before. One called "My Assistant" is not. Add a short description that explains what the assistant is for and who should use it. This description is visible to users when browsing or selecting agents, and helps them quickly identify whether a particular assistant is relevant to their task. *** ### Step 2: Write the System Prompt The system prompt is the most important part of your agent configuration. It is the instruction set that tells the assistant who it is, what it is for, how it should behave, and what boundaries it should operate within. Everything the assistant does flows from this prompt. A well-written system prompt typically includes: **A role definition.** Tell the assistant what it is. For example: "You are a legal document review assistant specialising in contract analysis for a European enterprise." **A description of the task.** Explain what the assistant is designed to do. Be specific. "Your role is to review uploaded contracts and identify liability clauses, termination conditions, and any non-standard terms that may require legal attention." **Tone and style guidance.** Define how the assistant should communicate. "Respond in a professional, concise tone. Use structured output with clear headings and bullet points where appropriate." **Scope and constraints.** Be explicit about what the assistant should and should not do. "Only respond to questions related to legal document review. If asked about topics outside this scope, politely redirect the user to the appropriate resource." **Output format instructions.** If the assistant should always return responses in a specific structure, define that here. This ensures consistency across every interaction, regardless of who is using it. Take your time with the system prompt. It is worth testing and refining it across several real interactions before sharing the assistant more broadly with your team. *** ### Step 3: Select a Model Choose the AI model your assistant will use to generate responses. Different models have different strengths, and the right choice depends on the nature of the task. For most business writing, summarisation, and communication tasks, a capable general-purpose frontier model will perform well. For tasks that require precise reasoning, structured analysis, or complex multi-step logic, a reasoning-optimised model may be more appropriate. For tasks involving sensitive or confidential data that should not leave GLBNXT infrastructure, a locally hosted open-source model is the right choice. Refer to the Model Selection & Switching section for guidance on choosing the right model for your use case. *** ### Step 4: Attach Knowledge and Documents If your assistant needs to answer questions based on specific content, such as your organisation's internal policies, a reference document, or a knowledge collection, you can attach that content during the configuration step. Documents and knowledge collections attached to an agent are available to every user who works with it, without them needing to upload the files themselves each time. This makes knowledge-grounded assistants significantly more consistent and easier to use across a team. For guidance on how document retrieval works within agents, refer to the Knowledge & Data section. *** ### Step 5: Configure Tools and Capabilities Depending on your interface configuration and the permissions enabled for your account, you may be able to equip your assistant with additional capabilities beyond standard text generation. These may include: * Web search, allowing the assistant to retrieve current information from the internet as part of its responses * Code execution, allowing the assistant to run and return the results of code as part of its workflow * Structured output generation, enabling the assistant to produce formatted data such as JSON or CSV alongside its text responses Enable only the capabilities that are genuinely needed for the assistant's purpose. Keeping the configuration focused produces a more reliable and predictable assistant. *** ### Step 6: Test Before Publishing Before making your assistant available to others, test it thoroughly across a range of real interactions. Use the prompts and scenarios your intended users are likely to bring to it, and evaluate the responses critically. Ask yourself: * Does the assistant stay within its defined scope? * Are the responses consistently formatted and structured as intended? * Does it handle edge cases or unexpected questions appropriately? * Is the tone and style right for the intended audience? Refine the system prompt based on what you observe. Even small adjustments to the wording of a system prompt can produce meaningful improvements in consistency and output quality. It is normal to go through several iterations before an assistant is ready for wider use. *** ### Step 7: Share Your Assistant Once you are satisfied with your assistant, you can make it available to your team. Depending on your permissions and interface configuration, you can share an agent with specific users, specific teams or groups, or make it available across your entire organisation. Sharing settings and access controls are managed through the role-based access control layer. If you need to share an agent beyond the scope your current permissions allow, contact your administrator. For guidance on managing shared agents and team access, see the Sharing Agents Across Your Team section. *** ### Tips for Effective Agent Configuration **Start simple and iterate.** A focused assistant with a clear, well-written system prompt will outperform a complex one with an overly ambitious scope. Build the core behaviour first, then add capabilities and knowledge as you develop a better understanding of how users interact with it. **Test with real users early.** Your assumptions about how the assistant will be used are rarely perfect. Getting real users to try it early in the process surfaces issues that are difficult to anticipate from the builder alone. **Review and refine over time.** Agents are not set-and-forget configurations. As your workflows evolve and you gather feedback from users, revisit the system prompt and configuration to keep the assistant performing at its best. **Document what your agent is for.** A clear name and description at the point of creation saves significant confusion later, particularly as your organisation builds a library of agents across multiple teams and use cases. --- # 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/building-your-first-agent.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.