08-15-2026, 03:30 PM
How to build an AI agent: A simple guide for anyone
Author: Samantha Kubota
Publisher: Microsoft Signal
Publication date: August 10, 2026
Microsoft’s guide explains AI agents as a step beyond ordinary AI chatbots: instead of merely answering questions, agents can take actions on a user’s behalf, such as monitoring inboxes, tracking deadlines, producing reports, routing messages, or responding to security incidents. The article emphasizes that building an agent should begin with the problem rather than the technology.
Users should identify a repetitive or time-consuming task, clearly define what outcome they want, and check whether an existing agent already solves the problem. Microsoft argues that even non-programmers can create useful agents through Microsoft 365 Copilot by describing what they want in natural language and then refining the resulting agent.
The next stage is giving the agent appropriate instructions, knowledge, and boundaries. An agent can be connected to sources such as emails, documents, SharePoint sites, websites, or company policies, while its expected outputs—reports, spreadsheets, presentations, responses, or code—can be specified in advance. Importantly, Microsoft recommends telling agents what they should not do, such as guessing missing information.
After creation, the agent should be tested against real situations and its instructions repeatedly refined. The overall message is that a useful AI agent does not need to be sophisticated or autonomous from the beginning: start with a narrowly defined workflow, create a working prototype, evaluate its behavior, and gradually expand its capabilities.
Key takeaways
ARTICLE
Author: Samantha Kubota
Publisher: Microsoft Signal
Publication date: August 10, 2026
Microsoft’s guide explains AI agents as a step beyond ordinary AI chatbots: instead of merely answering questions, agents can take actions on a user’s behalf, such as monitoring inboxes, tracking deadlines, producing reports, routing messages, or responding to security incidents. The article emphasizes that building an agent should begin with the problem rather than the technology.
Users should identify a repetitive or time-consuming task, clearly define what outcome they want, and check whether an existing agent already solves the problem. Microsoft argues that even non-programmers can create useful agents through Microsoft 365 Copilot by describing what they want in natural language and then refining the resulting agent.
The next stage is giving the agent appropriate instructions, knowledge, and boundaries. An agent can be connected to sources such as emails, documents, SharePoint sites, websites, or company policies, while its expected outputs—reports, spreadsheets, presentations, responses, or code—can be specified in advance. Importantly, Microsoft recommends telling agents what they should not do, such as guessing missing information.
After creation, the agent should be tested against real situations and its instructions repeatedly refined. The overall message is that a useful AI agent does not need to be sophisticated or autonomous from the beginning: start with a narrowly defined workflow, create a working prototype, evaluate its behavior, and gradually expand its capabilities.
Key takeaways
- Start with a real problem, not with AI technology itself.
- AI agents differ from chatbots because they can perform actions, not simply generate answers.
- Useful agents need good instructions, trusted data sources, defined outputs, and clear boundaries.
- Testing and iteration are essential; the goal is initially to build something useful rather than something perfect.
ARTICLE
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│ KONSTANTINOS MICHAILIDIS │
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