AI agents Courses

Every agent is built from the same building blocks: a language model as the reasoning engine, a set of tools it can call (search, APIs, databases, files), memory that keeps track of the context, and a control loop that lets the agent plan, act, evaluate the result and adjust course. Once you understand these building blocks, you can see through any agent product on the market, whatever it is called, and judge what it can actually do and what is just marketing.

Microsoft Copilot Studio gives you a visual way to design, extend and integrate your own agents that can automate processes, answer questions and interact with your company's data through connectors to Power Platform, Microsoft 365 and external systems. This lets power users and consultants build and maintain agents without needing a full development team, and with the governance Power Platform already provides.

If the agent needs to be built into your own systems, Azure AI Foundry Agent Service provides a managed platform with tool integration, threads and built-in traceability, while frameworks such as Semantic Kernel let you define the agent's functions, planning and memory directly in code. You learn to choose between managed services and frameworks based on your requirements for control, operations and integration, and to connect the agent securely to the systems it needs to work in.

Complex tasks are often best handled by having several specialized agents work together, each with its own area of responsibility: one that does research, one that produces, one that handles quality assurance. That requires orchestration: How are the subtasks divided up, how do the agents exchange results, and who decides when the goal has been reached? You learn the patterns behind multi-agent architectures, and when a single, well-designed agent is the better choice.

The more autonomy an agent gets, the more important the guardrails become. What can the agent do without approval, where does a human need to step in (human-in-the-loop), how are its actions logged, and how is it tested before it goes into production? You learn to assess use cases, define the scope of the agent's permissions and establish the controls that make it responsible to give an agent access to real systems and data. The organizational framework around this, from ownership to approval, is covered under AI strategy & governance.