# aitour26-resource-center **Repository Path**: mirrors_microsoft/aitour26-resource-center ## Basic Information - **Project Name**: aitour26-resource-center - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-13 - **Last Updated**: 2025-10-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

decorative banner

# [Microsoft AI Tour 2026](https://aitour.microsoft.com) ## 🔥AI Tour 26 Resource Center Thanks for attending Microsoft AI Tour 2026! Recordings, code and demos from all technical sessions from AI Tour are available from this repository. For beginners and experts, you can enhance your skill-building and stay engaged. Dive into the resources to further your knowledge and apply what you've learned in practical scenarios. ## AI Business Solutions ### Copilot and agents at work #### Breakout sessions * [BRK422 - Deploy and manage agents securely at scale with the Copilot Control System​](https://aka.ms/BRK422GHrepo) #### Workshops * [WRK523 - Copilot Ready: Strategy, Data, and Security](https://aka.ms/WRK523GHrepo) ### Innovate with Low Code AI and Agents #### Breakout sessions * [BRK431 - Advanced Agent Development with Copilot Studio](https://aka.ms/BRK431GHrepo) #### Workshops * [WRK532 - Building agentic solutions with Copilot Studio](https://aka.ms/WRK532GHrepo) ## Cloud and AI Platforms ### Innovate with Azure AI apps and agents #### Breakout sessions * [BRK441 - Build and launch AI agents fast with GitHub Models and Azure AI Foundry](https://aka.ms/BRK441GHrepo) * [BRK442 - GitHub Copilot as an AI Agent in the Developer Workflow](https://aka.ms/BRK442GHrepo) * [BRK443 - Efficient model customization with Azure AI Foundry](https://aka.ms/BRK443GHrepo) * [BRK444 - Advanced retrieval for your AI Apps and Agents on Azure](https://aka.ms/BRK444GHrepo) * [BRK445 - Building enterprise-ready AI Agents with Azure AI Foundry](https://aka.ms/BRK445GHrepo) * [BRK447 - Agentic use of GitHub Copilot within Visual Studio](https://aka.ms/BRK447GHrepo) #### Lightning talks * [LTG151 - Build trustworthy AI with systematic evaluations in Azure AI Foundry](https://aka.ms/LTG151GHrepo) * [LTG153 - Automate model selection with Azure AI Foundry Model Router](https://aka.ms/LTG153GHrepo) * [LTG152 - From protocol to practice: build and use your first MCP server](https://aka.ms/LTG152GHrepo) * [LTG154 - Breaking Down the AI Tour Advisor Agent](https://aka.ms/LTG154GHrepo) #### Workshops * [WRK540 - Unlock your agents’ potential with Model Context Protocol (MCP)](https://aka.ms/WRK540GHrepo) * [WRK541 - Real world code migration with GitHub Copilot Agent Mode](https://aka.ms/WRK541GHrepo) ### Unify Your Data Platform #### Breakout sessions * [BRK461 - What's new in Microsoft databases: empowering ai-driven app dev](https://aka.ms/BRK461GHrepo) * [BRK462 - Enable agentic AI apps with a unified data estate in Microsoft Fabric](https://aka.ms/BRK462GHrepo) #### Workshops * [WRK560 - Modernize your data estate with Fabric, Databricks, and AI Foundry](https://aka.ms/WRK560GHrepo) #### Lightning talks * [LTG155 - Fast Data Transformation in Fabric with OneLake and the Data Wrangler](https://aka.ms/LTG155GHrepo) * [LTG158 - Level up as a data expert with AI-ready databases and tools](https://aka.ms/LTG158GHrepo) ### Migrate and Modernize Your Estate #### Breakout Sessions * [BRK471 - AI Tools for Infrastructure Management](https://aka.ms/BRK471GHrepo) #### Workshops * [WRK570 - Improving Ops with Copilot in Azure and GitHub Copilot](https://aka.ms/WRK570GHrepo) ## Security #### Breakout Sessions * [BRK482 - Secure, Govern, and Scale Your AI Agents with Azure API Management](https://aka.ms/BRK482GHrepo) ### 📚 Continued Learning Resources | Resources | Links | Description | |:-------------------|:----------------------------------|:-------------------| | AI Tour 2026 Resource Center | [https://aka.ms/AITour26-Resource-Center](https://aka.ms/AITour26-Resource-Center?ocid=AITour26_resourcecenter_cnl) | Links to all repos for AI Tour 26 Sessions | | Azure AI Foundry Community Discord | [![Microsoft Azure AI Foundry Discord](https://dcbadge.limes.pink/api/server/ByRwuEEgH4)](https://discord.gg/Pwpvf3TWaw)| Connect with the Azure AI Foundry Community! | | Azure AI Foundry Developer Forum | [![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=adff2f&logoColor=fff)](https://aka.ms/foundry/forum)| Join the Azure AI Foundry Developer Forum! | | Learn at AI Tour | [https://aka.ms/LearnAtAITour](https://aka.ms/LearnAtAITour?ocid=AITour26_resourcecenter_cnl) | Continue learning on Microsoft Learn | ## Responsible AI 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](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](https://learn.microsoft.com/legal/cognitive-services/openai/transparency-note?tabs=text) 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](https://learn.microsoft.com/azure/ai-services/content-safety/overview) 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](https://learn.microsoft.com/azure/ai-services/content-safety/quickstart-text?tabs=visual-studio%2Clinux&pivots=programming-language-rest) 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](https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-metrics-built-in). You also have the ability to create and evaluate with [custom evaluators](https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#custom-evaluators). You can evaluate your AI application in your development environment using the [Azure AI Evaluation SDK](https://microsoft.github.io/promptflow/index.html). 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](https://learn.microsoft.com/azure/ai-studio/how-to/develop/flow-evaluate-sdk). Once you execute an evaluation run, you can [visualize the results in Azure AI Foundry portal ](https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-flow-results).