Tech Accelerator: Azure security and AI adoption
Plan, build, manage and optimize your Azure deployments and AI projects with a security-first mindset. Learn how Microsoft protects its platform and get in-depth technical guidance from Microsoft experts about how you can use various products and tools to identify security risks in your Azure environments, protect your infrastructure from security threats, secure your AI workloads, and more! April 22, 2025 - now on demand Q&A will remain open for all sessions through Friday, April 25! Security: An essential part of your Azure and AI journey Secure by design: Azure datacenter and hardware security AMA: Azure platform security Enhancing security for cloud migration How to secure your AI environment How to design and build secure AI projects Safeguard AI applications with Microsoft Defender for CloudTranslator within Azure Cognitive Services AMA
We are very excited to announce a Translator within Cognitive Services AMA! An AMA is a live text-based online event similar to a “YamJam” on Yammer or an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. Feel free to post your questions anytime in the comments below beforehand, if it fits your schedule or time zone better, though questions will not be answered until the live hour.Bring all your workloads to the edge with AKS Edge Essentials. Now Generally Available!
Announcing the General Availability (GA) of Azure Kubernetes Service (AKS) Edge Essentials! AKS Edge Essentials has been developed as a managed Kubernetes for the operational edge on small-footprint devices to orchestrated workloads and drive business optimization.Tech Accelerator: Mastering Azure and AI adoption
Join us to learn about the essential guidance, resources, products and tooling you need to accelerate your next Azure and AI project or enhance your existing Azure deployments. Get in-depth technical guidance from Microsoft experts to enhance the reliability, security and ongoing performance of your Azure workloads. Learn more about AMD products and solutions to accelerate cloud adoption. Now on demand! Best practices for secure and reliable Azure projects Govern, manage and secure your AI deployments How to run a successful Azure migration project Advance cloud infrastructure: Essentials with AMD on Azure Essentials to build and modernize AI applications on Azure Proactively design, deploy & monitor resilient Azure workloads Cloud platform security in an evolving threat landscapeBeyond Prompts: How Agentic AI is Redefining Human-AI Collaboration
The Shift from Reactive to Proactive AI As a passionate innovator in AI education, I’m on a mission to reimagine how we learn and build with AI—looking to craft intelligent agents that move beyond simple prompts to think, plan, and collaborate dynamically. Traditional AI systems rely heavily on prompt-based interactions—you ask a question, and the model responds. These systems are reactive, limited to single-turn tasks, and lack the ability to plan or adapt. This becomes a bottleneck in dynamic environments where tasks require multi-step reasoning, memory, and autonomy. Agentic AI changes the game. An agent is a structured system that uses a looped process to: Think – analyze inputs, reason about tasks, and plan actions. Act – choose and execute tools to complete tasks. Learn – optionally adapt based on feedback or outcomes. Unlike static workflows, agentic systems can: Make autonomous decisions Adapt to changing environments Collaborate with humans or other agents This shift enables AI to move from being a passive assistant to an active collaborator—capable of solving complex problems with minimal human intervention. What Is Agentic AI? Agentic AI refers to AI systems that go beyond static responses—they can reason, plan, act, and adapt autonomously. These agents operate in dynamic environments, making decisions and invoking tools to achieve goals with minimal human intervention. Some of the frameworks that can be used for Agentic AI include LangChain, Semantic Kernel, AutoGen, Crew AI, MetaGPT, etc. The frameworks can use Azure OpenAI, Anthropic Claude, Google Gemini, Mistral AI, Hugging Face Transformers, etc. Key Traits of Agentic AI Autonomy Agents can independently decide what actions to take based on context and goals. Unlike assistants, which support users, agents' complete tasks and drive outcomes. Memory Agents can retain both long-term and short-term context. This enables personalized and context-aware interactions across sessions. Planning Semantic Kernel agents use function calling to plan multi-step tasks. The AI can iteratively invoke functions, analyze results, and adjust its strategy—automating complex workflows. Adaptability Agents dynamically adjust their behavior based on user input, environmental changes, or feedback. This makes them suitable for real-world applications like task management, learning assistants, or research copilots. Frameworks That Enable Agentic AI Semantic Kernel: A flexible framework for building agents with skills, memory, and orchestration. Supports plugins, planning, and multi-agent collaboration. More information here: Semantic Kernel Agent Architecture. Azure AI Foundry: A managed platform for deploying secure, scalable agents with built-in governance and tool integration. More information here: Exploring the Semantic Kernel Azure AI Agent. LangGraph: A JavaScript-compatible SDK for building agentic apps with memory and tool-calling capabilities, ideal for web-based applications. More information here: Agentic app with LangGraph or Azure AI Foundry (Node.js) - Azure App Service. Copilot Studio: A low-code platform to build custom copilots and agentic workflows using generative AI, plugins, and orchestration. Ideal for enterprise-grade conversational agents. More information here: Building your own copilot with Copilot Studio. Microsoft 365 Copilot: Embeds agentic capabilities directly into productivity apps like Word, Excel, and Teams—enabling contextual, multi-step assistance across workflows. More information here: What is Microsoft 365 Copilot? Why It Matters: Real-World Impact Traditional Generative AI is like a calculator—you input a question, and it gives you an answer. It’s reactive, single-turn, and lacks context. While useful for quick tasks, it struggles with complexity, personalization, and continuity. Agentic AI, on the other hand, is like a smart teammate. It can: Understand goals Plan multi-step actions Remember past interactions Adapt to changing needs Generative AI vs. Agentic Systems Feature Generative AI Agentic AI Interaction Style One-shot responses Multi-turn, goal-driven Context Awareness Limited Persistent memory Task Execution Static Dynamic and autonomous Adaptability Low High (based on feedback/input) How Agentic AI Works — Agentic AI for Students Example Imagine a student named Alice preparing for her final exams. She uses a Smart Study Assistant powered by Agentic AI. Here's how the agent works behind the scenes: Skills / Functions These are the actions or the callable units of logic the agent can invoke to perform. The assistant has functions like: Summarize lecture notes Generate quiz questions Search academic papers Schedule study sessions Think of these as plug-and-play capabilities the agent can call when needed. Memory The agent remembers Alice’s: Past quiz scores Topics she struggled with Preferred study times This helps the assistant personalize recommendations and avoid repeating content she already knows. Planner Instead of doing everything at once, the agent: Breaks down Alice’s goal (“prepare for exams”) into steps Plans a week-by-week study schedule Decides which skills/functions to use at each stage It’s like having a tutor who builds a custom roadmap. Orchestrator This is the brain that coordinates everything. It decides when to use memory, which function to call, and how to adjust the plan if Alice misses a study session or scores low on a quiz. It ensures the agent behaves intelligently and adapts in real time. Conclusion Agentic AI marks a pivotal shift in how we interact with intelligent systems—from passive assistants to proactive collaborators. As we move beyond prompts, we unlock new possibilities for autonomy, adaptability, and human-AI synergy. Whether you're a developer, educator, or strategist, understanding agentic frameworks is no longer optional - it’s foundational. Here are the high-level steps to get started with Agentic AI using only official Microsoft resources, each with a direct link to the relevant documentation: Get Started with Agentic AI Understand Agentic AI Concepts - Begin by learning the fundamentals of AI agents, their architecture, and use cases. See: Explore the basics in this Microsoft Learn module Set Up Your Azure Environment - Create an Azure account and ensure you have the necessary roles (e.g., Azure AI Account Owner or Contributor). See: Quickstart guide for Azure AI Foundry Agent Service Create Your First Agent in Azure AI Foundry - Use the Foundry portal to create a project and deploy a default agent. Customize it with instructions and test it in the playground. See: Step-by-step agent creation in Azure AI Foundry Build an Agentic Web App with Semantic Kernel or Foundry - Follow a hands-on tutorial to integrate agentic capabilities into a .NET web app using Semantic Kernel or Azure AI Foundry. See: Tutorial: Build an agentic app with Semantic Kernel or Foundry Deploy and Test Your Agent - Use GitHub Codespaces or Azure Developer CLI to deploy your app and connect it to your agent. Validate functionality using OpenAPI tools and the agent playground. See: Deploy and test your agentic app For Further Learning: Develop generative AI apps with Azure OpenAI and Semantic Kernel Agentic app with Semantic Kernel or Azure AI Foundry (.NET) - Azure App Service AI Agent Orchestration Patterns - Azure Architecture Center Configuring Agents with Semantic Kernel Plugins Workflows with AI Agents and Models - Azure Logic Apps About the author: I'm Juliet Rajan, a Lead Technical Trainer and passionate innovator in AI education. I specialize in crafting gamified, visionary learning experiences and building intelligent agents that go beyond traditional prompt-based systems. My recent work explores agentic AI, autonomous copilots, and dynamic human-AI collaboration using platforms like Azure AI Foundry and Semantic Kernel.The Future of AI: Computer Use Agents Have Arrived
Discover the groundbreaking advancements in AI with Computer Use Agents (CUAs). In this blog, Marco Casalaina shares how to use the Responses API from Azure OpenAI Service, showcasing how CUAs can launch apps, navigate websites, and reason through tasks. Learn how CUAs utilize multimodal models for computer vision and AI frameworks to enhance automation. Explore the differences between CUAs and traditional Robotic Process Automation (RPA), and understand how CUAs can complement RPA systems. Dive into the future of automation and see how CUAs are set to revolutionize the way we interact with technology.How to Build AI Agents in 10 Lessons
Microsoft has released an excellent learning resource for anyone looking to dive into the world of AI agents: "AI Agents for Beginners". This comprehensive course is available free on GitHub. It is designed to teach the fundamentals of building AI agents, even if you are just starting out. What You'll Learn The course is structured into 10 lessons, covering a wide range of essential topics including: Agentic Frameworks: Understand the core structures and components used to build AI agents. Design Patterns: Learn proven approaches for designing effective and efficient AI agents. Retrieval Augmented Generation (RAG): Enhance AI agents by incorporating external knowledge. Building Trustworthy AI Agents: Discover techniques for creating AI agents that are reliable and safe. AI Agents in Production: Get insights into deploying and managing AI agents in real-world applications. Hands-On Experience The course includes practical code examples that utilize: Azure AI Foundry GitHub Models These examples help you learn how to interact with Language Models and use AI Agent frameworks and services from Microsoft, such as: Azure AI Agent Service Semantic Kernel Agent Framework AutoGen - A framework for building AI agents and applications Getting Started To get started, make sure you have the proper set-up. Here are the 10 lessons Intro to AI Agents and Agent Use Cases Exploring AI Agent Frameworks Understanding AI Agentic Design Principles Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Multi-Agent Design Patterns Metacognition in AI Agents AI Agents in Production Multi-Language Support To make learning accessible to a global audience, the course offers multi-language support. Get Started Today! If you are eager to learn about AI agents, this course is an excellent starting point. You can find the complete course materials on GitHub at AI Agents for Beginners.
