Microsoft Foundry for VS Code: January 2026 Update
Enhanced Workflow and Agent Experience The January 2026 update for Microsoft Foundry extension in VS Code brings a follow update to the capabilities we introduced during Ignite of last year. We’re excited to announce a set of powerful updates that make building and managing AI workflows in Azure AI Foundry even more seamless. These enhancements are designed to give developers greater flexibility, visibility, and control when working with multi-agent systems and workflows. Support for Multiple Workflows in the Visualizer Managing complex AI solutions often involves multiple workflows. With this update, the Workflow Visualizer now supports viewing and navigating multiple workflows in a single project. This makes it easier to design, debug, and optimize interconnected workflows without switching contexts. View and Test Prompt Agents in the Playground Prompt agents are a critical part of orchestrating intelligent behaviors. You can now view all prompt agents directly in the Playground and test them interactively. This feature helps you validate agent logic and iterate quickly, ensuring your prompts deliver the desired outcomes. Open Code files Transparency and customization are key for developers. We’ve introduced the ability to open sample code files for all agents, including: Prompt agents YAML-based workflows Hosted agents Foundry classic agents This gives you the ability to programmatically run agents, enabling adding these agents into your existing project. Separated Resource View for v1 and v2 Agents To reduce confusion and improve clarity, we’ve introduced a separated resource view for Foundry Classic resources and agents. This makes it simple to distinguish between legacy and new-generation agents, ensuring you always know which resources you’re working with. How to Get Started Download the extension here: Microsoft Foundry in VS Code Marketplace Get started with building agents and workflows with Microsoft Foundry in VS Code MS Learn Docs Feedback & Support These improvements are part of our ongoing commitment to deliver a developer-first experience in Microsoft Foundry. Whether you’re orchestrating multi-agent workflows or fine-tuning prompt logic, these features help you build smarter, faster, and with greater confidence. Try out the extensions and let us know what you think! File issues or feedback on our GitHub repo for Foundry extension. Your input helps us make continuous improvements.🚀 AI Toolkit for VS Code: January 2026 Update
Happy New Year! 🎆 We are kicking off 2026 with a major set of updates designed to streamline how you build, test, and deploy AI agents. This month, we’ve 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. 💡 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. 🔄 Enhanced AIAgentExpert: Our custom agent now has a deeper understanding of workflow code generation and evaluation planning/execution. 🧹Automatic 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. 🏗️ Major Enhancements to Agent Development Our v0.28.0 milestone release brought significant improvements to how agents are authored and authenticated. 🔒 Anthropic & Entra Auth Support We’ve 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. 🏢 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. 📊 Model Catalog: We’ve optimized the Model Catalog to prioritize Foundry models and improved general loading performance. 🏎️ 💻 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, we’ve removed the Windows AI API tab from the Model Catalog when running on Linux and macOS platforms. 🐛 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. 🚀 Getting Started Ready to experience the future of AI development? Here's how to get started: 📥 Download: Install the AI Toolkit from the Visual Studio Code Marketplace 📖 Learn: Explore our comprehensive AI Toolkit Documentation 🔍 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! 💻✨From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve 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 it’s 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 isn’t just about running models locally, it’s 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 don’t 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 You’ll 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 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 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 - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s 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.AI Dev Days 2025: Your Gateway to the Future of AI Development
What’s in Store? Day 1 – 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 – 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. You’ll recognise plenty of familiar faces in the lineup – don’t miss the chance to connect and learn from the best! 👉 Register now and share widely across your networks – there’s truly something for everyone! https://aka.ms/ai-dev-daysUpcoming Live Stream: Building AI Agents with the AI Toolkit & Microsoft Foundry
We’re at a moment where generative AI is shifting from single-prompt interactions to agents that can process visuals, store memory, and act. And the best way to understand that shift is to build something yourself! That’s exactly what we’re doing in my upcoming live stream on Building AI Agents with the AI Toolkit & Microsoft Foundry — a hands-on walkthrough of the full lab experience from Microsoft Ignite 2025! This session is designed for developers, makers, and anyone curious about how multimodal agents get from idea to working prototype. What we'll explore During the stream, I’ll walk through the core concepts and build steps from the lab, including: Setting Up Your Environment in Microsoft Foundry You’ll see how to create and configure your project, connect to models, and prepare your workspace using the AI Toolkit in VS Code. This lab makes it approachable, even if you’re new to Foundry or agent workflows. Testing Multimodal Inputs We’ll explore how the agent processes text and images, how the model interprets such input, and how that insight becomes part of its reasoning loop. During the stream, I’ll show you what strong visual prompts look like, where people usually get stuck, and how to shape the output you want. Designing an Agent System Prompt We’ll look at how to structure agent behavior and how a well-crafted system prompt becomes the foundation for consistent responses and accurate multimodal reasoning. This includes grounding, action definitions, and the type of instructions that help an agent combine text, vision, and reasoning capabilities. Iterating With the AI Toolkit This is where things get fun. We’ll use the AI Toolkit’s playground and debugging tools to observe the agent’s thought process, test different instructions, and evaluate its planning behavior. You’ll see why tools like trace view, structured output, and function definitions make iteration faster and more predictable. Expanding Beyond the Lab To close, we’ll talk through what it looks like to extend the agent: Adding new skills Changing how it plans Connecting it to additional data Turning the prototype into an application My goal is for you to take away a repeatable workflow, one you can reuse whether you’re building a creative tool, a developer agent, or something entirely new. The Bigger Picture Multimodal agents are becoming the new interface layer for apps: they can interpret images, understand context, take actions, and guide users through workflows that feel natural. If you understand how to prototype them, you understand how AI-powered products will be built in the next few years. This stream is for anyone who wants to experiment, learn by doing, and make sense of where AI tooling is headed. Date: Wednesday December 3, 2025 Time: 10AM – 11AM Pacific Link: https://aka.ms/AITGHC/Dec3/b View on DemandOn‑Device AI with Windows AI Foundry and Foundry Local
From “waiting” to “instant”- 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 isn’t - 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 On‑Device AI? Faster, more responsive apps: Local inference often reduces perceived latency and improves user experience. Privacy‑first 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, high‑volume tasks. This approach is especially useful in regulated industries, field‑work tools, and any app where users expect quick, on‑device responses. Hybrid Pattern for Real Apps On-device AI doesn’t replace the cloud, it complements it. Here’s how: Standalone On‑Device: Quick, private actions like document summarization, local search, and offline assistants. Cloud‑Enhanced (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 isn’t just a trend - it’s 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 you’re 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/Azure 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 credentials—all 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 rights—they'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, you’ll 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 experience—the 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 – 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 content—though our 1.4 million module completions last fiscal year and 35,000+ certifications awarded speak to scale—but 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 solutions—all 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 does—it 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 decisively—moving 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" Let’s 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 dynamically—if 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)How to Integrate Playwright MCP for AI-Driven Test Automation
Test automation has come a long way, from scripted flows to self-healing and now AI-driven testing. With the introduction of Model Context Protocol (MCP), Playwright can now interact with AI models and external tools to make smarter testing decisions. This guide walks you through integrating MCP with Playwright in VSCode, starting from the basics, enabling you to build smarter, adaptive tests today. What Is Playwright MCP? Playwright: An open-source framework for web testing and automation. It supports multiple browsers (Chromium, Firefox, and WebKit) and offers robust features like auto-wait, capturing screenshots, along with some great tooling like Codegen, Trace Viewer. MCP (Model Context Protocol): A protocol that enables external tools to communicate with AI models or services in a structured, secure way. By combining Playwright with MCP, you unlock: AI-assisted test generation. Dynamic test data. Smarter debugging and adaptive workflows. Why Integrate MCP with Playwright? AI-powered test generation: Reduce manual scripting. Dynamic context awareness: Tests adapt to real-time data. Improved debugging: AI can suggest fixes for failing tests. Smarter locator selection: AI helps pick stable, reliable selectors to reduce flaky tests. Natural language instructions: Write or trigger tests using plain English prompts. Getting Started in VS Code Prerequisites Node.js Download: nodejs.org Minimum version: v18.0.0 or higher (recommended: latest LTS) Check version: node --version Playwright Install Playwright: npm install @playwright/test Step 1: Create Project Folder mkdir playwrightMCP-demo cd playwrightMCP-demo Step 2: Initialize Project npm init playwright@latest Step 3: Install MCP Server for VS Code Navigate to GitHub - microsoft/playwright-mcp: Playwright MCP server and click install server for VS Code Search for 'MCP: Open user configuration' (type ‘>mcp’ in the search box) You will see a file mcp.json is created in your user -> app data folder, which is having the server details. { "servers": { "playwright": { "command": "npx", "args": [ "@playwright/mcp@latest" ], "type": "stdio" } }, "inputs": [] } Alternatively, install an MCP server directly GitHub MCP server registry using the Extensions view in VS Code. From GitHub MCP server registry Verify installation: Open Copilot Chat → select Agent Mode → click Configure Tools → confirm microsoft/playwright-mcp appears in the list. Step 4: Create a Simple Test Using MCP Once your project and MCP setup are ready in VS Code, you can create a simple test that demonstrates MCP’s capabilities. MCP can help in multiple scenarios, below is the example for Test Generation using AI: Scenario: AI-Assisted Test Generation- Use natural language prompts to generate Playwright tests automatically. Test Scenario - Validate that a user can switch the Playwright documentation language dropdown to Python, search for “Frames,” and navigate to the Frames documentation page. Confirm that the page heading correctly displays “Frames.” Sample Prompt to Use in VS Code (Copilot Agent Mode):Create a Playwright automated test in JavaScript that verifies navigation to the 'Frames' documentation page following below steps and be more specific about locators to avoid strict mode violation error Navigate to : Playwright documentation select “Python” from the dropdown options, labelled “Node.js” Type the keyword “Frames” into the search box. Click the search result for the Frames documentation page Verify that the page header reads “Frames”. Log success or provide a failure message with details. Copilot will generate the test automatically in your tests folder Step 5: Run Test npx playwright test Conclusion Integrating Playwright with MCP in VS Code helps you build smarter, adaptive tests without adding complexity. Start small, follow best practices, and scale as you grow. Note - Installation steps may vary depending on your environment. Refer to MCP Registry · GitHub for the latest instructions.
