Study Buddy: Learning Data Science and Machine Learning with an AI Sidekick
If you've ever wished for a friendly companion to guide you through the world of data science and machine learning, you're not alone. As part of the "For Beginners" curriculum, I recently built a Study Buddy Agent, an AI-powered assistant designed to help learners explore data science interactively, intuitively, and joyfully. Why a Study Buddy? Learning something new can be overwhelming, especially when you're navigating complex topics like machine learning, statistics, or Python programming. The Study Buddy Agent is here to change that. It brings the curriculum to life by answering questions, offering explanations, and nudging learners toward deeper understanding, all in a conversational format. Think of it as your AI-powered lab partner: always available, never judgmental, and endlessly curious. Built with chatmodes, Powered by Purpose The agent lives inside a .chatmodes file in the https://github.com/microsoft/Data-Science-For-Beginners/blob/main/.github/chatmodes/study-mode.chatmode.md. This file defines how the agent behaves, what tone it uses, and how it interacts with learners. I designed it to be friendly, encouraging, and beginner-first—just like the curriculum itself. It’s not just about answering questions. The Study Buddy is trained to: Reinforce key concepts from the curriculum Offer hints and nudges when learners get stuck Encourage exploration and experimentation Celebrate progress and milestones What’s Under the Hood? The agent uses GitHub Copilot's chatmode, which allows developers to define custom behaviors for AI agents. By aligning the agent’s responses with the curriculum’s learning objectives, we ensure that learners stay on track while enjoying the flexibility of conversational learning. How You Can Use It YouTube Video here: Study Buddy - Data Science AI Sidekick Clone the repo: Head to the https://github.com/microsoft/Data-Science-For-Beginners and clone it locally or use Codespaces. Open the GitHub Copilot Chat, and select Study Buddy: This will activate the Study Buddy. Start chatting: Ask questions, explore topics, and let the agent guide you. What’s Next? This is just the beginning. I’m exploring ways to: Expand the agent to other beginner curriculums (Web Dev, AI, IoT) Integrate feedback loops so learners can shape the agent’s evolution Final Thoughts In my role, I believe learning should be inclusive, empowering, and fun. The Study Buddy Agent is a small step toward that vision, a way to make data science feel less like a mountain and more like a hike with a good friend. Try it out, share your feedback, and let’s keep building tools that make learning magical. Join us on Discord to share your feedback.Introducing the Microsoft Agent Framework
Introducing the Microsoft Agent Framework: A Unified Foundation for AI Agents and Workflows The landscape of AI development is evolving rapidly, and Microsoft is at the forefront with the release of the Microsoft Agent Framework an open-source SDK designed to empower developers to build intelligent, multi-agent systems with ease and precision. Whether you're working in .NET or Python, this framework offers a unified, extensible foundation that merges the best of Semantic Kernel and AutoGen, while introducing powerful new capabilities for agent orchestration and workflow design. Introducing Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps | Azure AI Foundry Blog Introducing Microsoft Agent Framework | Microsoft Azure Blog Why Another Agent Framework? Both Semantic Kernel and AutoGen have pioneered agentic development, Semantic Kernel with its enterprise-grade features and AutoGen with its research-driven abstractions. The Microsoft Agent Framework is the next generation of both, built by the same teams to unify their strengths: AutoGen’s simplicity in multi-agent orchestration. Semantic Kernel’s robustness in thread-based state management, telemetry, and type safety. New capabilities like graph-based workflows, checkpointing, and human-in-the-loop support This convergence means developers no longer have to choose between experimentation and production. The Agent Framework is designed to scale from single-agent prototypes to complex, enterprise-ready systems Core Capabilities AI Agents AI agents are autonomous entities powered by LLMs that can process user inputs, make decisions, call tools and MCP servers, and generate responses. They support providers like Azure OpenAI, OpenAI, and Azure AI, and can be enhanced with: Agent threads for state management. Context providers for memory. Middleware for action interception. MCP clients for tool integration Use cases include customer support, education, code generation, research assistance, and more—especially where tasks are dynamic and underspecified. Workflows Workflows are graph-based orchestrations that connect multiple agents and functions to perform complex, multi-step tasks. They support: Type-based routing Conditional logic Checkpointing Human-in-the-loop interactions Multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic) Workflows are ideal for structured, long-running processes that require reliability and modularity. Developer Experience The Agent Framework is designed to be intuitive and powerful: Installation: Python: pip install agent-framework .NET: dotnet add package Microsoft.Agents.AI Integration: Works with Foundry SDK, MCP SDK, A2A SDK, and M365 Copilot Agents Samples and Manifests: Explore declarative agent manifests and code samples Learning Resources: Microsoft Learn modules AI Agents for Beginners AI Show demos Azure AI Foundry Discord community Migration and Compatibility If you're currently using Semantic Kernel or AutoGen, migration guides are available to help you transition smoothly. The framework is designed to be backward-compatible where possible, and future updates will continue to support community contributions via the GitHub repository. Important Considerations The Agent Framework is in public preview. Feedback and issues are welcome on the GitHub repository. When integrating with third-party servers or agents, review data sharing practices and compliance boundaries carefully. The Microsoft Agent Framework marks a pivotal moment in AI development, bringing together research innovation and enterprise readiness into a single, open-source foundation. Whether you're building your first agent or orchestrating a fleet of them, this framework gives you the tools to do it safely, scalably, and intelligently. Ready to get started? Download the SDK, explore the documentation, and join the community shaping the future of AI agents.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.Level up your Python Gen AI Skills from our free nine-part YouTube series!
Want to learn how to use generative AI models in your Python applications? We're putting on a series of nine live streams, in both English and Spanish, all about generative AI. We'll cover large language models, embedding models, vision models, introduce techniques like RAG, function calling, and structured outputs, and show you how to build Agents and MCP servers. Plus we'll talk about AI safety and evaluations, to make sure all your models and applications are producing safe outputs. 🔗 Register for the entire series. In addition to the live streams, you can also join a weekly office hours in our AI Discord to ask any questions that don't get answered in the chat. You can also scroll down to learn about each live stream and register for individual sessions. See you in the streams! 👋🏻 Large Language Models 7 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor Join us for the first session in our Python + AI series! In this session, we'll talk about Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We'll use Python to interact with LLMs using popular packages like the OpenAI SDK and Langchain. We'll experiment with prompt engineering and few-shot examples to improve our outputs. We'll also show how to build a full stack app powered by LLMs, and explain the importance of concurrency and streaming for user-facing AI apps. Vector embeddings 8 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In our second session of the Python + AI series, we'll dive into a different kind of model: the vector embedding model. A vector embedding is a way to encode a text or image as an array of floating point numbers. Vector embeddings make it possible to perform similarity search on many kinds of content. In this session, we'll explore different vector embedding models, like the OpenAI text-embedding-3 series, with both visualizations and Python code. We'll compare distance metrics, use quantization to reduce vector size, and try out multimodal embedding models. Retrieval Augmented Generation 9 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Vision models 14 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor Our third stream in the Python + AI series is all about vision models! Vision models are LLMs that can accept both text and images, like GPT 4o and 4o-mini. You can use those models for image captioning, data extraction, question-answering, classification, and more! We'll use Python to send images to vision models, build a basic chat-on-images app, and build a multimodal search engine. Structured outputs 15 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly. We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows. Quality and safety 16 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor Now that we're more than halfway through our Python + AI series, we're covering a crucial topic: how to use AI safely, and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. Our focus will be on Azure tools that make it easier to put safe AI systems into production. We'll show how to configure the Azure AI Content Safety system when working with Azure AI models, and how to handle those errors in Python code. Then we'll use the Azure AI Evaluation SDK to evaluate the safety and quality of the output from our LLM. Tool calling 21 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor Now that we're more than halfway through our Python + AI series, we're covering a crucial topic: how to use AI safely, and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. Our focus will be on Azure tools that make it easier to put safe AI systems into production. We'll show how to configure the Azure AI Content Safety system when working with Azure AI models, and how to handle those errors in Python code. Then we'll use the Azure AI Evaluation SDK to evaluate the safety and quality of the output from our LLM. AI agents 22 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor For the penultimate session of our Python + AI series, we're building AI agents! We'll use many of the most popular Python AI agent frameworks: Langgraph, Semantic Kernel, Autogen, Pydantic AI, and more. Our agents will start simple and then ramp up in complexity, demonstrating different architectures like hand-offs, round-robin, supervisor, graphs, and ReAct. Model Context Protocol 23 October, 2025 | 5:00 PM - 6:00 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In the final session of our Python + AI series, we're diving into the hottest technology of 2025: MCP, Model Context Protocol. This open protocol makes it easy to extend AI agents and chatbots with custom functionality, to make them more powerful and flexible. We'll show how to use the official Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we'll build our own MCP client to consume the server. Finally, we'll discover how easy it is to point popular AI agent frameworks like Langgraph, Pydantic AI, and Semantic Kernel at MCP servers. With great power comes great responsibility, so we will briefly discuss the many security risks that come with MCP, both as a user and developer.
Azure AI foundry SDK-Tool Approval Not Triggered When Using ConnectedAgentTool() Between Agents
I am building an orchestration workflow in Azure AI Foundry using the Python SDK. Each agent uses tools exposed via an MCP server (deployed in Azure container app), and individual agents work perfectly when run independently — tool approval is triggered, and execution proceeds as expected. I have a main agent which orchestrates the flow of these individual agents.However, when I connect one agent to another using ConnectedAgentTool(), the tool approval flow never occurs, and orchestration halts. All I see is the run status as IN-PROGRESS for some time and then exits. The downstream (child) agent is never invoked. I have tried mcp_tool.set_approval_mode("never") , but that didn't help. Auto-Approval Implementation: I have implemented a polling loop that checks the run status and auto-approves any requires_action events. async def poll_run_until_complete(project_client: AIProjectClient, thread_id: str, run_id: str): """ Polls the run until completion. Auto-approves any tool calls encountered. """ while True: run = await project_client.agents.runs.get(thread_id=thread_id, run_id=run_id) status = getattr(run, "status", None) print(f"[poll] Run {run_id} status: {status}") # Completed states if status in ("succeeded", "failed", "cancelled", "completed"): print(f"[poll] Final run status: {status}") if status == "failed": print("Run last_error:", getattr(run, "last_error", None)) return run # Auto-approve any tool calls if status == "requires_action" and isinstance(getattr(run, "required_action", None), SubmitToolApprovalAction): submit_action = run.required_action.submit_tool_approval tool_calls = getattr(submit_action, "tool_calls", []) or [] if not tool_calls: print("[poll] requires_action but no tool_calls found. Waiting...") else: approvals = [] for tc in tool_calls: print(f"[poll] Auto-approving tool call: {tc.id} name={tc.name} args={tc.arguments}") approvals.append(ToolApproval(tool_call_id=tc.id, approve=True)) if approvals: await project_client.agents.runs.submit_tool_outputs( thread_id=thread_id, run_id=run_id, tool_approvals=approvals ) print("[poll] Submitted tool approvals.") else: # Debug: Inspect run steps if stuck run_steps = [s async for s in project_client.agents.run_steps.list(thread_id=thread_id, run_id=run_id)] if run_steps: for step in run_steps: sid = getattr(step, "id", None) sstatus = getattr(step, "status", None) print(f" step: id={sid} status={sstatus}") step_details = getattr(step, "step_details", None) if step_details: tool_calls = getattr(step_details, "tool_calls", None) if tool_calls: for call in tool_calls: print(f" tool_call id={getattr(call,'id',None)} name={getattr(call,'name',None)} args={getattr(call,'arguments',None)} output={getattr(call,'output',None)}") await asyncio.sleep(1) This code works and auto-approves tool calls for MCP tools. But while using ConnectedAgentTool(), the run never enters requires_action — so no approvals are requested, and the orchestration halts. Environment: azure-ai-agents==1.2.0b4 azure-ai-projects==1.1.0b4 Python: 3.11.13 Auth: DefaultAzureCredential Notes: MCP tools work and trigger approval normally when directly attached. and I ndividual agents function as expected in standalone runs. Can anyone help here..!Modernizing legacy Java project using GitHub Copilot App Modernization
In this blog, we explore how the GitHub Copilot App Modernization – Upgrade for Java can streamline the process of modernizing legacy Java applications. To put the tool to the test, we selected the widely known Spring Boot Pet Clinic project, originally built with Java 8 and Spring Boot 2.x. Using GitHub Copilot Upgrade for Java’s modernization capabilities, we successfully upgraded the project to Java 21 and Spring Boot 3.4.x. This post highlights the upgrade journey, showcases the tool’s capabilities, and shares practical tips and lessons learned along the way.Foundry Fridays: Your Front-Row Seat to Azure AI Innovation
🔥 Foundry Fridays: Your Front-Row Seat to Azure AI Innovation Are you ready to go beyond the blog posts and docs and get your questions answered directly by the minds behind Azure AI? Then mark your calendars for Foundry Fridays a weekly Ask Me Anything (AMA) series hosted on the Azure AI Foundry Discord. Every Friday at 1:30 PM ET, the Azure AI team opens the floor to developers, researchers, and enthusiasts for a 30-minute live AMA with the experts building the future of AI at Microsoft. Whether you're curious about model fine-tuning, local inference, agentic workflows, or the latest in open-source tooling—Foundry Fridays is where the real-time insights happen. 🎙️ Why Join Foundry Fridays? Direct Access to Experts: Ask your questions live to Principal PMs, researchers, and engineers from the Azure AI Foundry team. Fresh Topics Weekly: Each session spotlights a new theme from model routing and MCP registries to SAMBA architectures and AI agent security. Community-Driven: These aren’t lectures—they’re conversations. Bring your curiosity, share your feedback, and help shape the future of Azure AI. No Slides, Just Substance: It’s raw, real, and refreshingly unscripted. You’ll hear what’s working, what’s coming, and what’s still being figured out. Episode is hosted by community leaders like Nitya Narasimhan and Lee Stott, who guide the conversation and ensure your questions get the spotlight they deserve you can watch all the Monday Model Series on Demand at https://aka.ms/model-mondays and get ready for Season 3 of Model Mondays every Monday at 1.30pm ET. 📈 Why It Matters Foundry Fridays isn’t just another event it’s a community catalyst. Join our communty hear from experts and share your experiences of using Azure AI Tools and Services. 🚀 How to Join Join the Discord: aka.ms/model-mondays/discord Find the AMA: Head to the Events #community-calls and #model-mondays channel or check the pinned events. Ask Anything: Come with questions, ideas, or just listen in. No registration required. Want a sneak peek at what’s coming? Check the Foundry Fridays schedule or follow the Azure AI Foundry Blog for recaps and resources. 💬 Final Thoughts Whether you're building with Azure AI, exploring open-source models, or just curious about what’s next—Foundry Fridays is your chance to connect, learn, and grow with the community. So grab your headphones, fire up Discord, and let’s build the future of AI—together. 🗓️ Fridays | 1:30 PM ET 📍 Azure AI Foundry Discord 🔗 Join Now
