Copilot Studio – designing and deploying your own AI agents

1. Introduction to Copilot Studio

Microsoft Copilot Studio is positioned here in relation to Microsoft 365 Copilot and Copilot Chat, so participants can distinguish platform roles and use cases. The section also explains Agentic AI, the architecture and interface of the environment, and typical business applications of organizational AI agents.

• what Microsoft Copilot Studio is and the platform architecture,
• differences between Microsoft 365 Copilot, Copilot Chat and Copilot Studio,
• possibilities for creating AI agents for organizations,
• Agentic AI – what intelligent and autonomous agents are,
• examples of AI agent applications in business,
• overview of the user interface and work environment.

2. Designing an AI agent

A useful agent starts with a clear objective, defined scope and a deliberate conversation model. This section covers Topics, Instructions and Conversation Flow, shows how AI Instructions steer model behavior, and applies prompt engineering practices to plan architecture and information flow.

• defining the objective and scope of the agent's operation,
• designing conversation scenarios,
• creating Topics, Instructions and Conversation Flow,
• AI Instructions – controlling the model's behavior and defining the agent's operating principles,
• best practices of Prompt Engineering for AI agents,
• planning the agent architecture and information flow.

3. Building the logic of agent operation

Building behavior in Copilot Studio requires more than drafting prompts. Here the work centers on questions, answers and actions, with conditions, branching, variables and conversation memory shaping process logic, while exception handling and generative responses support autonomous agents performing more complex tasks.

• questions, answers and actions,
• conditions, branches and process logic,
• use of variables and conversation memory,
• generative responses (Generative AI),
• handling exceptions and error scenarios,
• designing autonomous agents performing complex tasks.

4. Knowledge Sources and the use of RAG

Reliable answers depend on how organizational content is attached and retrieved. Participants work with Knowledge Sources from SharePoint, OneDrive, websites and office documents, use RAG to ground responses, and apply quality controls while also drawing on Microsoft Graph as a source of business data.

• adding Knowledge Sources,
• SharePoint,
• OneDrive,
• websites,
• PDF, Word and PowerPoint documents,
• use of the RAG (Retrieval-Augmented Generation) mechanism,
• controlling the quality of responses generated by AI,
• use of Microsoft Graph as a source of organizational data.

5. Integrations and automation

Connecting the agent to business processes extends it beyond conversation into execution. The section explains Power Automate, Microsoft Graph API, connectors, REST API and custom Actions, then shows how MCP supports cooperation with external tools and multi-service agent architectures.

• integration with Power Automate,
• use of Microsoft Graph API,
• Microsoft connectors and external services,
• REST API,
• creating custom Actions,
• MCP (Model Context Protocol) – integration of agents with external tools and systems,
• building agents cooperating with multiple services.

6. Testing and deployment

Before release, the agent needs verification in its target environment and a structured deployment path. The focus is on response analysis, debugging and publication across Microsoft Teams, websites and business applications, with versioning and environment management included to keep changes controlled.

• testing the agent in the working environment,
• analysis of responses and debugging,
• publishing in Microsoft Teams,
• publishing on websites,
• deployment in business applications,
• version and environment management.

7. Management, security and development of agents

Once an agent is in use, effective operation depends on visibility, control and clear governance. This section covers conversation history, performance and cost monitoring, along with permissions, data security, compliance and responsible AI practices that support iterative improvement across the solution lifecycle.

• monitoring conversation history,
• analysis of the agent's effectiveness,
• monitoring the costs of using AI models and agents,
• data security and user permissions,
• AI agent governance and solution lifecycle management,
• best practices regarding compliance, security and responsible use of AI,
• iterative development and optimization of agents.

8. Practical workshop – building your own AI agent

Working through a complete build ties the earlier topics into one implementation path. Participants define a business scenario, create the agent, connect knowledge sources, add Microsoft Graph and Power Automate, then test, publish and review results with recommendations for further development.

• designing a business scenario,
• creating an intelligent AI agent,
• connecting knowledge sources,
• using Microsoft Graph and Power Automate,
• implementing AI Instructions,
• testing and optimizing operation,
• publishing the agent,
• discussion of results and recommendations for further development.