How to build an AI agent: A simple guide for anyone

Microsoft describes AI agents as tools that can perform actions beyond answering questions, such as disabling compromised accounts or drafting incident reports, unlike traditional chat apps. The company highlights their potential to automate repetitive tasks like tracking deadlines or monitoring shared inboxes. Users can build these agents without coding by following a guide that leverages Microsoft 365 Copilot, starting with a clear definition of the problem and desired outcome. The process begins in Microsoft 365 Copilot Chat under the 'Agents' section, where users can create and test prototypes immediately.

The guide advises starting with the job to be done rather than the technology, emphasizing collaboration with colleagues to clarify needs and define outcomes. It suggests checking for prebuilt agents or existing AI models that may already address the task. For those with limited coding experience, Microsoft 365 Copilot offers a straightforward path to building agents, while more advanced customization may require developer tools. An example provided involves an agent that sorts client emails, drafts responses, and routes messages to appropriate team members based on predefined criteria.

Microsoft explains that users can describe their requirements in plain language, and Copilot will generate a draft agent for review. The next step involves refining the agent’s instructions, including its behavior, tone, and tasks, to align with specific workflows. For instance, an email response team could train an agent to categorize messages by urgency and route them accordingly, using approved language for routine queries while flagging sensitive issues for human review.

The final step involves connecting the agent to relevant data sources, such as emails, documents, or SharePoint sites, and defining the output format, whether reports, spreadsheets, or responses. Users can attach documents directly or use the chatbot interface to guide the process. Microsoft recommends testing the agent in real scenarios and refining its instructions as needed. The guide notes that agents can be iteratively improved over time, with capabilities expanded or transitioned to more advanced tools as required.