đ AI Toolkit for VS Code: January 2026 Update
Happy New Year! đ We are kicking off 2026 with a major set of updates designed to streamline how you build, test, and deploy AI agents. This month, weâve focused on aligning with the latest GitHub Copilot standards, introducing powerful new debugging tools, and enhancing our support for enterprise-grade models via Microsoft Foundry. đĄ From Copilot Instructions to Agent Skills The biggest architectural shift following the latest VS Code Copilot standards, in v0.28.1 is the transition from Copilot Instructions to Copilot Skills. This transition has equipped GitHub Copilot specialized skills on developing AI agents using Microsoft Foundry and Agent Framework in a cost-efficient way. In AI Toolkit, we have migrated our Copilot Tools from the Custom Instructions to Agent Skills. This change allows for a more capable integration within GitHub Copilot Chat. đ Enhanced AIAgentExpert: Our custom agent now has a deeper understanding of workflow code generation and evaluation planning/execution. đ§šAutomatic Migration: When you upgrade to v0.28.1, the toolkit will automatically clean up your old instructions to ensure a seamless transition to the new skills-based framework. đď¸ Major Enhancements to Agent Development Our v0.28.0 milestone release brought significant improvements to how agents are authored and authenticated. đ Anthropic & Entra Auth Support Weâve expanded the Agent Builder and Playground to support Anthropic models using Entra Auth types. This provides enterprise developers with a more secure way to leverage Claude models within the Agent Framework while maintaining strict authentication standards. đ˘ Foundry-First Development We are prioritizing the Microsoft Foundry ecosystem to provide a more robust development experience: Foundry v2: Code generation for agents now defaults to Foundry v2. ⥠Eval Tool: You can now generate evaluation code directly within the toolkit to create and run evaluations in Microsoft Foundry. đ Model Catalog: Weâve optimized the Model Catalog to prioritize Foundry models and improved general loading performance. đď¸ đť Performance and Local Models For developers building on Windows, we continue to optimize the local model experience: Profiling for Windows ML: Version 0.28.0 introduces profiling features for Windows ML-based local models, allowing you to monitor performance and resource utilization directly within VS Code. Platform Optimization: To keep the interface clean, weâve removed the Windows AI API tab from the Model Catalog when running on Linux and macOS platforms. đ Squashing Bugs & Polishing the Experience Codespaces Fix: Resolved a crash occurring when selecting images in the Playground while using GitHub Codespaces. Resource Management: Fixed a delay where newly added models wouldn't immediately appear in the "My Resources" view. Claude Compatibility: Fixed an issue where non-empty content was required for Claude models when used via the AI Toolkit in GitHub Copilot. đ Getting Started Ready to experience the future of AI development? Here's how to get started: đĽ Download: Install the AI Toolkit from the Visual Studio Code Marketplace đ Learn: Explore our comprehensive AI Toolkit Documentation đ Discover: Check out the complete changelog for v0.24.0 We'd love to hear from you! Whether it's a feature request, bug report, or feedback on your experience, join the conversation and contribute directly on our GitHub repository. Happy Coding! đťâ¨From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, weâve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint itâs now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isnât just about running models locally, itâs about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that donât break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What Youâll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration đ 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview ⢠Industry Applications ⢠SLM Introduction ⢠Learning Objectives Beginner 1-2 hrs đ 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals ⢠Real World Case Studies ⢠Implementation Guide ⢠Edge Deployment Beginner 3-4 hrs đ§ 02 SLM Model Foundations Model families & architecture Phi Family ⢠Qwen Family ⢠Gemma Family ⢠BitNET ⢠ΟModel ⢠Phi-Silica Beginner 4-5 hrs đ 03 SLM Deployment Practice Local & cloud deployment Advanced Learning ⢠Local Environment ⢠Cloud Deployment Intermediate 4-5 hrs âď¸ 04 Model Optimization Toolkit Cross-platform optimization Introduction ⢠Llama.cpp ⢠Microsoft Olive ⢠OpenVINO ⢠Apple MLX ⢠Workflow Synthesis Intermediate 5-6 hrs đ§ 05 SLMOps Production Production operations SLMOps Introduction ⢠Model Distillation ⢠Fine-tuning ⢠Production Deployment Advanced 5-6 hrs đ¤ 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction ⢠Function Calling ⢠Model Context Protocol Advanced 4-5 hrs đť 07 Platform Implementation Cross-platform samples AI Toolkit ⢠Foundry Local ⢠Windows Development Advanced 3-4 hrs đ 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - đ§ Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 𧊠ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - đŽ DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - đĽď¸ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isnât just about inference, itâs about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.AI Upskilling Framework Level 3 Building
The Global AI Community is excited to bring you the latest updates on AI Upskilling Framework Level 3 Building, straight from Microsoft Ignite! This session dives deep into advanced concepts for building agentic workflows and showcases new announcements that will help developers accelerate their Agentic AI journey.Exploring the Future of AI Agents with Microsoft Foundry
Why Agentic AI Matters AI agents are no longer a distant visionâtheyâre here and transforming how businesses operate. According to industry analysts: Over 1 billion AI agents are expected to be in use by 2028. 80% of organisations plan to integrate agents within the next 2â3 years. By 2026, 40% of enterprise apps will include task-specific AI agents. Why this surge? Agents address critical challenges such as inefficiencies in manual processes, human error, lack of visibility, and scalability issues. They enable autonomous decision-making, with projections suggesting that by 2028, half of day-to-day work decisions will be made autonomously. From Chatbots to Intelligent Agents As Mary Joe highlighted, early chatbots relied on rigid rules and regular expressions, often leading to frustrating user experiences. The introduction of large language models (LLMs) changed the game, making interactions more natural. But true autonomy, where systems act on our behalf, required more than conversational AI. Agentic AI combines: Reasoning and planning capabilities. Tools and APIs for real-world actions. Memory for learning and improving over time. This evolution moves us beyond simple input-output interactions to intelligent systems that can execute workflows, validate data, and deliver outcomes. Microsoft Foundry: Your Platform for Building Agents Microsoft Foundry offers a Platform-as-a-Service (PaaS) approach for creating AI agents, striking a balance between control and ease of use. Key components include: Model Catalogue: Access models from OpenAI, Anthropic, Mistral, and more. Foundry Agent Service: Build and customise agents with integrated tools. Foundry IQ: Knowledge grounding for accurate responses. Control Plane: Ensures safety, trust, and observability in production. Whether you need full control (Infrastructure-as-a-Service) or simplicity (Software-as-a-Service via Copilot Studio), Foundry provides flexibility for diverse scenarios. What Makes an AI Solution Agentic? Unlike traditional AI apps that perform narrow tasks (e.g., extracting text from receipts), agentic solutions: Analyse inputs using LLMs and system instructions. Integrate tools for actions like file search, code execution, or API calls. Retain memory for contextual learning. Operate autonomously across workflows. Real-World Use Cases Agentic AI unlocks new possibilities across industries: Expense Management: Automate claims and approvals. Employee Onboarding: Personalised learning paths and skills navigation. Customer Support: Intelligent assistants for FAQs and troubleshooting. Data Analytics: Interactive insights and reporting with Fabric agents. Multi-agent systems can coordinate complex tasks, with specialised agents handling subtasks under a central orchestrator. Getting Started with Microsoft Foundry Creating your first agent is simple: Sign in at https://ai.azure.com and create a Foundry project. Select a model (e.g., GPT-4.1 mini) and configure deployment options. Customise instructions to define your agentâs persona and tasks. Add tools like file search or code interpreter for extended functionality. Test and iterate using the agent playground, then export code to Visual Studio Code for deployment. For detailed guidance, explore the https://learn.microsoft.com/training. Follow the skilling plan for this series Plans | Microsoft Learn Get started with AI Agents https://aka.ms/ai-agents-fundamentals Join the Community Stay connected and keep learning: Discord: Engage with developers building agents. https://aka.ms/foundry/discord GitHub Discussions: Share ideas and troubleshoot. https://aka.ms/foundrydevs Office Hours: Get direct support from product teams. Final Thoughts Agentic AI is reshaping the way we work, enabling systems to act, learn, and collaborate. With Microsoft Foundry, developers have the tools to build secure, scalable, and intelligent agents today not tomorrow. Join the sessions at https://aka.ms/AzureSkilling-Ignite/25AI Dev Days 2025: Your Gateway to the Future of AI Development
Whatâs in Store? Day 1 â 10 December: Video Link Building AI Applications with Azure, GitHub, and Foundry Explore cutting-edge topics like: Agentic DevOps Azure SRE Agent Microsoft Foundry MCP Models for AI innovation Day 2 â 11 December Agenda: Video Link Using AI to Boost Developer Productivity Get hands-on with: Agent HQ VS Code & Visual Studio 2026 GitHub Copilot Coding Agent App Modernisation Strategies Why Join? Hands-on Labs: Apply the latest product features immediately. Highlights from Microsoft Ignite & GitHub Universe 2025: Stay ahead of the curve. Global Reach: Local-language workshops for LATAM and EMEA coming soon. Youâll recognise plenty of familiar faces in the lineup â donât miss the chance to connect and learn from the best! đ Register now and share widely across your networks â thereâs truly something for everyone! https://aka.ms/ai-dev-daysOnâDevice AI with Windows AI Foundry and Foundry Local
From âwaitingâ to âinstantâ- without sending data away AI is everywhere, but speed, privacy, and reliability are critical. Users expect instant answers without compromise. On-device AI makes that possible: fast, private and available, even when the network isnât - empowering apps to deliver seamless experiences. Imagine an intelligent assistant that works in seconds, without sending a text to the cloud. This approach brings speed and data control to the places that need it most; while still letting you tap into cloud power when it makes sense. Windows AI Foundry: A Local Home for Models Windows AI Foundry is a developer toolkit that makes it simple to run AI models directly on Windows devices. It uses ONNX Runtime under the hood and can leverage CPU, GPU (via DirectML), or NPU acceleration, without requiring you to manage those details. The principle is straightforward: Keep the model and the data on the same device. Inference becomes faster, and data stays local by default unless you explicitly choose to use the cloud. Foundry Local Foundry Local is the engine that powers this experience. Think of it as local AI runtime - fast, private, and easy to integrate into an app. Why Adopt OnâDevice AI? Faster, more responsive apps: Local inference often reduces perceived latency and improves user experience. Privacyâfirst by design: Keep sensitive data on the device; avoid cloud round trips unless the user opts in. Offline capability: An app can provide AI features even without a network connection. Cost control: Reduce cloud compute and data costs for common, highâvolume tasks. This approach is especially useful in regulated industries, fieldâwork tools, and any app where users expect quick, onâdevice responses. Hybrid Pattern for Real Apps On-device AI doesnât replace the cloud, it complements it. Hereâs how: Standalone OnâDevice: Quick, private actions like document summarization, local search, and offline assistants. CloudâEnhanced (Optional): Large-context models, up-to-date knowledge, or heavy multimodal workloads. Design an app to keep data local by default and surface cloud options transparently with user consent and clear disclosures. Windows AI Foundry supports hybrid workflows: Use Foundry Local for real-time inference. Sync with Azure AI services for model updates, telemetry, and advanced analytics. Implement fallback strategies for resource-intensive scenarios. Application Workflow Code Example using Foundry Local: 1. Only On-Device: Tries Foundry Local first, falls back to ONNX if foundry_runtime.check_foundry_available(): # Use on-device Foundry Local models try: answer = foundry_runtime.run_inference(question, context) return answer, source="Foundry Local (On-Device)" except Exception as e: logger.warning(f"Foundry failed: {e}, trying ONNX...") if onnx_model.is_loaded(): # Fallback to local BERT ONNX model try: answer = bert_model.get_answer(question, context) return answer, source="BERT ONNX (On-Device)" except Exception as e: logger.warning(f"ONNX failed: {e}") return "Error: No local AI available" 2. Hybrid approach: On-device first, cloud as last resort def get_answer(question, context): """ Priority order: 1. Foundry Local (best: advanced + private) 2. ONNX Runtime (good: fast + private) 3. Cloud API (fallback: requires internet, less private) # in case of Hybrid approach, based on real-time scenario """ if foundry_runtime.check_foundry_available(): # Use on-device Foundry Local models try: answer = foundry_runtime.run_inference(question, context) return answer, source="Foundry Local (On-Device)" except Exception as e: logger.warning(f"Foundry failed: {e}, trying ONNX...") if onnx_model.is_loaded(): # Fallback to local BERT ONNX model try: answer = bert_model.get_answer(question, context) return answer, source="BERT ONNX (On-Device)" except Exception as e: logger.warning(f"ONNX failed: {e}, trying cloud...") # Last resort: Cloud API (requires internet) if network_available(): try: import requests response = requests.post( '{BASE_URL_AI_CHAT_COMPLETION}', headers={'Authorization': f'Bearer {API_KEY}'}, json={ 'model': '{MODEL_NAME}', 'messages': [{ 'role': 'user', 'content': f'Context: {context}\n\nQuestion: {question}' }] }, timeout=10 ) answer = response.json()['choices'][0]['message']['content'] return answer, source="Cloud API (Online)" except Exception as e: return "Error: No AI runtime available", source="Failed" else: return "Error: No internet and no local AI available", source="Offline" Demo Project Output: Foundry Local answering context-based questions offline : The Foundry Local engine ran the Phi-4-mini model offline and retrieved context-based data. : The Foundry Local engine ran the Phi-4-mini model offline and mentioned that there is no answer. Practical Use Cases Privacy-First Reading Assistant: Summarize documents locally without sending text to the cloud. Healthcare Apps: Analyze medical data on-device for compliance. Financial Tools: Risk scoring without exposing sensitive financial data. IoT & Edge Devices: Real-time anomaly detection without network dependency. Conclusion On-device AI isnât just a trend - itâs a shift toward smarter, faster, and more secure applications. With Windows AI Foundry and Foundry Local, developers can deliver experiences that respect user specific data, reduce latency, and work even when connectivity fails. By combining local inference with optional cloud enhancements, you get the best of both worlds: instant performance and scalable intelligence. Whether youâre creating document summarizers, offline assistants, or compliance-ready solutions, this approach ensures your apps stay responsive, reliable, and user-centric. References Get started with Foundry Local - Foundry Local | Microsoft Learn What is Windows AI Foundry? | Microsoft Learn https://devblogs.microsoft.com/foundry/unlock-instant-on-device-ai-with-foundry-local/Unlocking Your First AI Solution on Azure: Practical Paths for Developers of All Backgrounds
Over the past several months, Iâve spent hundreds of hours working directly with teamsâfrom small startups to mid-market innovatorsâwho share the same aspiration: âWe want to use AI, but where do we start?â This question comes up everywhere. It crosses industries, geographies, skill levels, and team sizes. And as developers, we often feel the pressure to âsolve AIâ end-to-endâmodel selection, prompt engineering, security, deployment pipelines, integrationâŚ. The list is long, and the learning curve can feel even longer. But hereâs what weâve learned through our work in the SMB space and what we recently shared at Microsoft Ignite (Session OD1210). The first mile of AI doesnât have to be complex. You donât need an army of engineers, and you donât need to start from scratch. You just need the right path. In our Ignite on-demand session with UnifyCloud, we demonstrated two fast, developer-friendly ways to get your first AI workload running on Azureâboth grounded in real-world patterns we see every day. Path 1: Build Quickly with Microsoft Foundry Templates Microsoft Foundry gives developers pre-built, customizable templates that dramatically reduce setup time. In the session, I walked through how to deploy a fully functioning AI chatbot using: Azure AI Foundry GitHub (via the Azure Samples âGet Started with AI Chatâ repo) Azure Cloudshell for deployment And zero specialized infra prep With five lines of code and a few clicks, you can spin up a secure internal chatbot tailored for your business. Want responses scoped to your internal content? Want control over the model, costs, or safety filters? Want to plug in your own data sources like SharePoint, Blob Storage, or uploaded docs? You can do all of thatâeasily and on your terms. This âbuild fastâ path is ideal for: Developers who want control and extensibility Teams validating AI use cases Scenarios where data governance matters Lightweight experimentation without heavy architecture upfront And most importantly, you can scale it later. Path 2: Buy a Production-Ready Solution from a Trusted Partner Not every team wants to build. Not every team has the time, the resources, or the desire to compose their own AI stack. Thatâs why we showcased the âbuyâ path with UnifyCloudâs AI Factory, a Marketplace-listed solution that lets customers deploy mature AI capabilities directly into their Azure environment, complete with optional support, management, and best practices. In the demo, UnifyCloudâs founder Vivek Bhatnagar walked through: How to navigate Microsoft Marketplace How to evaluate solution listings How to review pricing plans and support tiers How to deploy a partner-built AI app with just a few clicks How customers can accelerate their time to value without implementation overhead This path is perfect when you want: A production-ready AI solution A supported, maintained experience Minimal engineering lift Faster time to outcome Why Azure? Why Now? During the session, we also outlined three reasons developers are choosing Azure for their first AI workloads: 1. Secure, governed, safe by design Azure mitigates risk with always-on guardrails and built-in commitments to security, privacy, and policy-based control. 2. Built for production with a complete AI platform From models to agents to tools and data integrations, Azure provides an enterprise-grade environment developers can trust. 3. Developer-first innovation with agentic DevOps Azure puts developers at the center, integrating AI across the software development lifecycle to help teams build faster and smarter. The Session: Build or BuyâTwo Paths, One Goal Whether you build using Azure AI Foundry or buy through Marketplace, the goal is the same: Help teams get to their first AI solution quickly, confidently, and securely. You donât need a massive budget. You donât need deep ML experience. You donât need a full-time AI team. What you need is a path that matches your skills, your constraints, and your timeline. Watch the Full Ignite Session You can watch the full session on-demand now also on YouTube: OD1201 â âUnlock Your First AI Solution on Azureâ It includes: The full build and buy demos Partner perspectives Deployment walkthroughs And guidance you can take back to your teams today If you want to explore the same build path we showed at Ignite: âĄď¸ Azure Samples â Get Started with AI Chat https://github.com/Azure-Samples/get-started-with-ai-chat Deploy it, customize it, attach your data sources, and extend it. Itâs a great starting point. If youâre curious about the Marketplace path: âĄď¸ Search for âUnifyCloud AI Factoryâ on Microsoft Marketplace Youâll see support offerings, solution details, and deployment instructions. Closing Thought The gap between wanting to adopt AI and actually running AI in production is shrinking fast. Azure makes it possible for teams, especially those without deep AI experience, to take meaningful steps today. No perfect architecture required. No million-dollar budget. No wait for a future-state roadmap. Just two practical paths: Build quickly. Buy confidently. Start now. If you have questions, ideas, or want to share what youâre building, feel free to reach out here in the Developer Community. Iâd love to hear what youâre creating. â Joshua Huang Microsoft AzureAzure Skilling at Microsoft Ignite 2025
The energy at Microsoft Ignite was unmistakable. Developers, architects, and technical decision-makers converged in San Francisco to explore the latest innovations in cloud technology, AI applications, and data platforms. Beyond the keynotes and product announcements was something even more valuable: an integrated skilling ecosystem designed to transform how you build with Azure. This year Azure Skilling at Microsoft Ignite 2025 brought together distinct learning experiences, over 150+ hands-on labs, and multiple pathways to industry-recognized credentialsâall designed to help you master skills that matter most in today's AI-driven cloud landscape. Just Launched at Ignite Microsoft Ignite 2025 offered an exceptional array of learning opportunities, each designed to meet developers anywhere on the skilling journey. Whether you joined us in-person or on-demand in the virtual experience, multiple touchpoints are available to deepen your Azure expertise. Ignite 2025 is in the books, but you can still engage with the latest Microsoft skilling opportunities, including: The Azure Skills Challenge provides a gamified learning experience that lets you compete while completing task-based achievements across Azure's most critical technologies. These challenges aren't just about badges and bragging rightsâthey're carefully designed to help you advance technical skills and prepare for Microsoft role-based certifications. The competitive element adds urgency and motivation, turning learning into an engaging race against the clock and your peers. For those seeking structured guidance, Plans on Learn offer curated sets of content designed to help you achieve specific learning outcomes. These carefully assembled learning journeys include built-in milestones, progress tracking, and optional email reminders to keep you on track. Each plan represents 12-15 hours of focused learning, taking you from concept to capability in areas like AI application development, data platform modernization, or infrastructure optimization. The Microsoft Reactor Azure Skilling Series, running December 3-11, brings skilling to life through engaging video content, mixing regular programming with special Ignite-specific episodes. This series will deliver technical readiness and programming guidance in a livestream presentation that's more digestible than traditional documentation. Whether you're catching episodes live with interactive Q&A or watching on-demand later, youâll get world-class instruction that makes complex topics approachable. Beyond Ignite: Your Continuous Learning Journey Here's the critical insight that separates Ignite attendees who transform their careers from those who simply collect swag: the real learning begins after the event ends. Microsoft Ignite is your launchpad, not your destination. Every module you start, every lab you complete, and every challenge you tackle connects to a comprehensive learning ecosystem on Microsoft Learn that's available 24/7, 365 days a year. Think of Ignite as your intensive immersion experienceâthe moment when you gain context, build momentum, and identify the skills that will have the biggest impact on your work. What you do in the weeks and months following determines whether that momentum compounds into career-defining expertise or dissipates into business as usual. For those targeting career advancement through formal credentials, Microsoft Certifications, Applied Skills and AI Skills Navigator, provide globally recognized validation of your expertise. Applied Skills focus on scenario-based competencies, demonstrating that you can build and deploy solutions, not simply answer theoretical questions. Certifications cover role-based scenarios for developers, data engineers, AI engineers, and solution architects. The assessment experiences include performance-based testing in dedicated Azure tenants where you complete real configuration and development tasks. And finally, the NEW AI Skills Navigator is an agentic learning space, bringing together AI-powered skilling experiences and credentials in a single, unified experience with Microsoft, LinkedIn Learning and GitHub â all in one spot Why This Matters: The Competitive Context The cloud skills race is intensifying. While our competitors offer robust training and content, Microsoft's differentiation comes not from having more contentâthough our 1.4 million module completions last fiscal year and 35,000+ certifications awarded speak to scaleâbut from integration of services to orchestrate workflows. Only Microsoft offers a truly unified ecosystem where GitHub Copilot accelerates your development, Azure AI services power your applications, and Azure platform services deploy and scale your solutionsâall backed by integrated skilling content that teaches you to maximize this connected experience. When you continue your learning journey after Ignite, you're not just accumulating technical knowledge. You're developing fluency in an integrated development environment that no competitor can replicate. You're learning to leverage AI-powered development tools, cloud-native architectures, and enterprise-grade security in ways that compound each other's value. This unified expertise is what transforms individual developers into force-multipliers for their organizations. Start Now, Build Momentum, Never Stop Microsoft Ignite 2025 offered the chance to compress months of learning into days of intensive, hands-on experience, but you can still take part through the on-demand videos, the Global Ignite Skills Challenge, visiting the GitHub repos for the /Ignite25 labs, the Reactor Azure Skilling Series, and the curated Plans on Learn provide multiple entry points regardless of your current skill level or preferred learning style. But remember: the developers who extract the most value from Ignite are those who treat the event as the beginning, not the culmination, of their learning journey. They join hackathons, contribute to GitHub repositories, and engage with the Azure community on Discord and technical forums. The question isn't whether you'll learn something valuable from Microsoft Ignite 2025-that's guaranteed. The question is whether you'll convert that learning into sustained momentum that compounds over months and years into career-defining expertise. The ecosystem is here. The content is ready. Your skilling journey doesn't end when Ignite doesâit accelerates.From Concept to Code: Building Production-Ready Multi-Agent Systems with Microsoft Foundry
We have reached a critical inflection point in AI development. Within the Microsoft Foundry ecosystem, the core value proposition of "Agents" is shifting decisivelyâmoving from passive content generation to active task execution and process automation. These are no longer just conversational interfaces. They are intelligent entities capable of connecting models, data, and tools to actively execute complex business logic. To support this evolution, Microsoft has introduced a powerful suite of capabilities: the Microsoft Agent Framework for sophisticated orchestration, the Agent V2 SDK, and integrated Microsoft Foundry VSCode Extensions. These innovations provide the tooling necessary to bridge the gap between theoretical research and secure, scalable enterprise landing. But how do you turn these separate components into a cohesive business solution? That is the challenge we address today. This post dives into the practical application of these tools, demonstrating how to connect the dots and transform complex multi-agent concepts into deployed reality. The Scenario: Recruitment through an "Agentic Lens" Letâs ground this theoretical discussion with a real-world scenario that perfectly models a multi-agent environment: The Recruitment Process. By examining recruitment through an agentic lens, we can identify distinct entities with specific mandates: The Recruiter Agent: Tasked with setting boundary conditions (job requirements) and preparing data retrieval mechanisms (interview questions). The Applicant Agent: Objective is to process incoming queries and synthesize the best possible output to meet the recruiter's acceptance criteria. Phase 1: Design Achieving Orchestration via Microsoft Foundry Workflows To bridge the gap between our scenario and technical reality, we start with Foundry Workflows. Workflows serves as the visual integration environment within Foundry. It allows you to build declarative pipelines that seamlessly combine deterministic business logic with the probabilistic nature of autonomous AI agents. By adopting this visual, low-code paradigm, you eliminate the need to write complex orchestration logic from scratch. Workflows empowers you to coordinate specialized agents intuitively, creating adaptive systems that solve complex business problems collaboratively. Visually Orchestrating the Cycle Microsoft Foundry provides an intuitive, web-based drag-and-drop interface. This canvas allows you to integrate specialized AI agents alongside standard procedural logic blocks, transforming abstract ideas into executable processes without writing extensive glue code. To translate our recruitment scenario into a functional workflow, we follow a structured approach: Agent Prerequisites: We pre-configure our specialized agents within Foundry. We create a Recruiter Agent (prompted to generate evaluation criteria) and an Applicant Agent (prompted to synthesize responses). Orchestrating the Interaction: We drag these nodes onto the board and define the data flow. The process begins with the Recruiter generating questions, piping that output directly as input for the Applicant agent. Adding Business Logic: A true workflow requires decision-making. We introduce control flow logic, such as IF/ELSE conditional blocks, to evaluate the recruiter's questions based on predefined criteria. This allows the workflow to branch dynamicallyâif satisfied, the candidate answers the questions; if not, the questions are regenerated. Alternative: YAML Configuration For developers who prefer a code-first approach or wish to rapidly replicate this logic across environments, Foundry allows you to export the underlying YAML. kind: workflow trigger: kind: OnConversationStart id: trigger_wf actions: - kind: SetVariable id: action-1763742724000 variable: Local.LatestMessage value: =UserMessage(System.LastMessageText) - kind: InvokeAzureAgent id: action-1763736666888 agent: name: HiringManager input: messages: =System.LastMessage output: autoSend: true messages: Local.LatestMessage - kind: Question variable: Local.Input id: action-1763737142539 entity: StringPrebuiltEntity skipQuestionMode: SkipOnFirstExecutionIfVariableHasValue prompt: Boss, can you confirm this ? - kind: ConditionGroup conditions: - condition: =Local.Input="Yes" actions: - kind: InvokeAzureAgent id: action-1763744279421 agent: name: ApplyAgent input: messages: =Local.LatestMessage output: autoSend: true messages: Local.LatestMessage - kind: EndConversation id: action-1763740066007 id: if-action-1763736954795-0 id: action-1763736954795 elseActions: - kind: GotoAction actionId: action-1763736666888 id: action-1763737425562 id: "" name: HRDemo description: "" Simulating the End-to-End Process Once constructed, Foundry provides a robust, built-in testing environment. You can trigger the workflow with sample input data to simulate the end-to-end cycle. This allows you to debug hand-offs and interactions in real-time before writing a single line of application code. Phase 2: Develop From Cloud Canvas to Local Code with VSCode Foundry Workflows excels at rapid prototyping. However, a visual UI is rarely sufficient for enterprise-grade production. The critical question becomes: How do we integrate these visual definitions into a rigorous Software Development Lifecycle (SDLC)? While the cloud portal is ideal for design, enterprise application delivery happens in the local IDE. The Microsoft Foundry VSCode Extension bridges this gap. This extension allows developers to: Sync: Pull down workflow definitions from the cloud to your local machine. Inspect: Review the underlying logic in your preferred environment. Scaffold: Rapidly generate the underlying code structures needed to run the flow. This accelerates the shift from "understanding" the flow to "implementing" it. Phase 3: Deploy Productionizing Intelligence with the Microsoft Agent Framework Once the multi-agent orchestration has been validated locally, the final step is transforming it into a shipping application. This is where the Microsoft Agent Framework shines as a runtime engine. It natively ingests the declarative Workflow definitions (YAML) exported from Foundry. This allows artifacts from the prototyping phase to be directly promoted to application deployment. By simply referencing the workflow configuration libraries, you can "hydrate" the entire multi-agent system with minimal boilerplate. Here is the code required to initialize and run the workflow within your application. Note - Check the source code https://github.com/microsoft/Agent-Framework-Samples/tree/main/09.Cases/MicrosoftFoundryWithAITKAndMAF Summary: The Journey from Conversation to Action Microsoft Foundry is more than just a toolbox; it is a comprehensive solution designed to bridge the chasm between theoretical AI research and secure, scalable enterprise applications. In this post, we walked through the three critical stages of modern AI development: Design (Low-Code): Leveraging Foundry Workflows to visually orchestrate specialized agents (Recruiter vs. Applicant) mixed with deterministic business rules. Develop (Local SDLC): Utilizing the VSCode Extension to break down the barriers between the cloud canvas and the local IDE, enabling seamless synchronization and debugging. Deploy (Native Runtime): Using the Microsoft Agent Framework to ingest declarative YAML, realizing the promise of "Configuration as Code" and eliminating tedious logic rewriting. By following this path, developers can move beyond simple content generation and build adaptive, multi-agent systems that drive real business value. Learning Resoures What's Microsoft Foundry (https://learn.microsoft.com/azure/ai-foundry/what-is-azure-ai-foundry?view=foundry) Work with Declarative (Low-code) Agent workflows in Visual Studio Code (preview) (https://learn.microsoft.com/azure/ai-foundry/agents/how-to/vs-code-agents-workflow-low-code?view=foundry) Microsoft Agent Framework(https://github.com/microsoft/agent-framework) Microsoft Foundry VSCode Extension(https://marketplace.visualstudio.com/items?itemName=TeamsDevApp.vscode-ai-foundry)AI Toolkit Extension Pack for Visual Studio Code: Ignite 2025 Update
Unlock the Latest Agentic App Capabilities The Ignite 2025 update delivers a major leap forward for the AI Toolkit extension pack in VS Code, introducing a unified, end-to-end environment for building, visualizing, and deploying agentic applications to Microsoft Foundry, and the addition of Anthropicâs frontier Claude models in the Model Catalog! This release enables developers to build and debug locally in VS Code, then deploy to the cloud with a single click. Seamlessly switch between VS Code and the Foundry portal for visualization, orchestration, and evaluation, creating a smooth roundtrip workflow that accelerates innovation and delivers a truly unified AI development experience. Download the http://aka.ms/aitoolkit today and start building next-generation agentic apps in VS Code! What Can You Do with the AI Toolkit Extension Pack? Access Anthropic models in the Model Catalog Following the Microsoft, NVIDIA and Anthropic strategic partnerships announcement today, we are excited to share that Anthropicâs frontier Claude models including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5, are now integrated into the AI Toolkit, providing even more choices and flexibility when building intelligent applications and AI agents. Build AI Agents Using GitHub Copilot Scaffold agent applications using best-practice patterns, tool-calling examples, tracing hooks, and test scaffolds, all powered by Copilot and aligned with the Microsoft Agent Framework. Generate agent code in Python or .NET, giving you flexibility to target your preferred runtime. Build and Customize YAML Workflows Design YAML-based workflows in the Foundry portal, then continue editing and testing directly in VS Code. To customize your YAML-based workflows, instantly convert it to Agent Framework code using GitHub Copilot. Upgrade from declarative design to code-first customization without starting from scratch. Visualize Multi-Agent Workflows Envision your code-based agent workflows with an interactive graph visualizer that reveals each component and how they connect Watch in real-time how each node lights up as you run your agent. Use the visualizer to understand and debug complex agent graphs, making iteration fast and intuitive. Experiment, Debug, and Evaluate Locally Use the Hosted Agents Playground to quickly interact with your agents on your development machine. Leverage local tracing support to debug reasoning steps, tool calls, and latency hotspotsâso you can quickly diagnose and fix issues. Define metrics, tasks, and datasets for agent evaluation, then implement metrics using the Foundry Evaluation SDK and orchestrate evaluations runs with the help of Copilot. Seamless Integration Across Environments Jump from Foundry Portal to VS Code Web for a development environment in your preferred code editor setting. Open YAML workflows, playgrounds, and agent templates directly in VS Code for editing and deployment. How to Get Started Install the AI Toolkit extension pack from the VS Code marketplace. Check out documentation. Get started with building workflows with Microsoft Foundry in VS Code 1. Work with Hosted (Pro-code) Agent workflows in VS Code 2. Work with Declarative (Low-code) Agent workflows in VS Code Feedback & Support Try out the extensions and let us know what you think! File issues or feedback on our GitHub repo for Foundry extension and AI Toolkit extension. Your input helps us make continuous improvements.
