This session dives into advanced agent development with Microsoft Copilot Studio. It's designed for makers and developers ready to move beyond the basics and orchestrate intelligent, autonomous agents at scale. Whether you're streamlining operations, enhancing customer service, or transforming internal processes, this session will equip you with the tools and strategies to build agents that deliver real business impact.
By the end of this session, learners understand how to:
- Build agents that reason, plan, and act across complex workflows using generative orchestration.
- Leverage multi-agent collaboration to drive real-time decision-making and automation.
- Integrate advanced capabilities like deep knowledge grounding, enhanced analytics, and no-code testing.
- Extend agents with pro-code tools such as Microsoft Foundry and Visual Studio for full-stack customization.
- Microsoft Copilot Studio
- Microsoft Foundry
| Resource | Path |
|---|---|
| Presenter Guide (full) | session-delivery-resources/readme.md |
| Environment Setup & Data Import | src/README.md |
| Slides | BRK431_Presentation.pptx |
| Full Session Recording | Recording |
| Resources | Links | Description |
|---|---|---|
| Copilot Studio Resources | https://aka.ms/copilotstudio-getstarted | |
| AI Tour 2026 Resource Center | https://aka.ms/AITour26-Resource-Center | Links to all repos for AI Tour 26 Sessions |
| Microsoft Foundry Community Discord | Connect with the Microsoft Foundry Community! | |
| Learn at AI Tour | https://aka.ms/LearnAtAITour | Continue learning on Microsoft Learn |
 April Dunnam 📢 |
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Foundry portal, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using Performance and Quality and Risk and Safety evaluators. You also have the ability to create and evaluate with custom evaluators.
You can evaluate your AI application in your development environment using the Azure AI Evaluation SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Foundry portal.