Building ShadowQuest: A Multi-Agent RPG
Artificial Intelligence is rapidly evolving beyond traditional chatbots. Today, developers are building intelligent systems where multiple AI agents collaborate, retrieve knowledge, and solve problems together. Microsoft's Agents League Hackathon provided the perfect opportunity to explore this new approach through the Reasoning Agents challenge. For this challenge, I built ShadowQuest, a fantasy role-playing game (RPG) powered by Microsoft Foundry, Foundry IQ, Azure AI Search, GPT-4.1, and GitHub Copilot. The project demonstrates how specialized AI agents can work together while using Retrieval-Augmented Generation (RAG) to deliver accurate and context-aware responses. About the Challenge Microsoft Agents League is a global developer challenge designed to encourage developers to build intelligent AI applications using Microsoft's latest AI technologies. Participants could choose from three tracks: Creative Apps, Reasoning Agents, and Enterprise Agents. I selected the Reasoning Agents track because I wanted to explore how multiple AI agents could collaborate instead of relying on a single large language model. Another important requirement for this year's challenge was integrating at least one Microsoft Intelligence Layer. For ShadowQuest, I chose Foundry IQ as the project's intelligence layer. The Idea Behind ShadowQuest Fantasy RPGs are built around storytelling, exploration, and collaboration between different characters. Every character usually has a unique role, whether it's a warrior protecting the team, a mage interpreting magical knowledge, or a rogue discovering hidden paths. I wanted to recreate this experience using AI. Instead of building one AI assistant responsible for everything, I designed a system where multiple specialized agents collaborate to create a richer and more immersive adventure. ShadowQuest is set in a fantasy world filled with magical artifacts, forgotten kingdoms, mysterious locations, and story-driven quests. Players can ask questions about the world, explore different locations, and learn about the game's lore through conversations with AI agents. Building the Multi-Agent Architecture The architecture follows a simple but scalable design. At the center of the system is the Game Master Agent, which acts as the orchestrator. Every player interaction starts with the Game Master. It receives the player's request, determines what information is needed, retrieves additional knowledge when required, and generates the final response. Supporting the Game Master are three specialized agents: Warrior Agent – Focuses on combat strategy and tactical decisions. Mage Agent – Provides magical knowledge, world lore, and information about ancient artifacts. Rogue Agent – Specializes in exploration, investigation, and discovering hidden information. Each agent has a clearly defined responsibility, making the system easier to understand, maintain, and extend in the future. Using Foundry IQ as the Knowledge Layer One of the most important parts of the project was integrating Foundry IQ. Instead of storing every piece of game information inside prompts, I created a dedicated knowledge base containing information about characters, magical artifacts, locations, quests, and the history of the ShadowQuest world. This approach separates knowledge from reasoning. Whenever a player asks a question, the Game Master Agent first retrieves relevant information from the knowledge base before generating a response. This ensures that answers remain consistent with the game's world while reducing hallucinations. Foundry IQ became the central source of truth for the entire project, making it easy to manage and expand the game world without constantly modifying prompts. Azure AI Search and Retrieval-Augmented Generation To enable intelligent retrieval, I connected Foundry IQ with Azure AI Search. The RPG documents were indexed, and vector embeddings were generated using Microsoft's embedding models. This enables semantic search, allowing the system to understand the meaning behind a player's question instead of relying only on keyword matching. For example, if a player asks about a magical relic without mentioning its exact name, Azure AI Search can still retrieve the correct information based on semantic similarity. The complete workflow looks like this: The player submits a question. The Game Master Agent receives the request. Foundry IQ queries Azure AI Search. Relevant documents are retrieved. GPT-4.1 generates a grounded response using the retrieved context. This Retrieval-Augmented Generation (RAG) approach significantly improves the quality and reliability of responses. Accelerating Development with GitHub Copilot GitHub Copilot played an important role throughout the development process. It helped generate Python classes, improve documentation, create helper functions, and speed up repetitive coding tasks. During the live demonstration, I also showed how Copilot could quickly generate a new Healer Agent, demonstrating how AI-assisted development makes it easier to extend a multi-agent application while maintaining a consistent architecture. Rather than replacing the developer, Copilot acted as an intelligent coding assistant, allowing me to focus more on architecture and design decisions. Demonstrating ShadowQuest During the Microsoft Agents League Reasoning Agents Battle, I demonstrated the Game Master Agent by asking questions about the ShadowQuest world, magical artifacts, and game lore. One of the most interesting parts of the demonstration was observing the retrieval process. Before generating a response, the Game Master Agent called the knowledge retrieval function through Foundry IQ. This confirmed that the system was retrieving relevant information from the indexed knowledge base rather than relying only on GPT-4.1's internal knowledge. This demonstrated how RAG can create more grounded, reliable, and context-aware AI experiences. Lessons Learned Building ShadowQuest taught me that designing multi-agent systems is as much about architecture as it is about AI models. Clearly defining responsibilities for each agent made the application easier to maintain and opened the door for future expansion. I also learned how valuable Retrieval-Augmented Generation can be for applications that depend on structured knowledge. Separating reasoning from knowledge allows AI systems to remain accurate while making it easier to update information over time. Finally, participating in the Microsoft Agents League was an incredible opportunity to experiment with Microsoft's latest AI technologies, learn from other developers, and share ideas with a global community passionate about agentic AI. Looking Ahead ShadowQuest is only the beginning. In future iterations, I plan to expand the project by introducing additional agents such as a Merchant Agent and Healer Agent, implementing persistent player memory, adding dynamic quest generation, improving combat mechanics, and enabling deeper collaboration between agents. These improvements will make the game world more immersive while continuing to explore the possibilities of agent-based AI systems. Conclusion ShadowQuest demonstrates how Microsoft Foundry, Foundry IQ, Azure AI Search, GPT-4.1, and GitHub Copilot can be combined to build intelligent multi-agent applications. More importantly, the project reinforced an important idea: the future of AI is not a single assistant performing every task, but a team of specialized agents collaborating with shared knowledge to solve increasingly complex problems. Participating in the Microsoft Agents League was an inspiring experience that allowed me to explore the next generation of AI development while building a project that combines storytelling, reasoning, and knowledge retrieval. I look forward to continuing this journey and discovering new ways to build intelligent applications using Microsoft's growing AI ecosystem.Building Reliable AI Coding Workflows Using Modular AI Agent Optimization
Artificial Intelligence is rapidly transforming the modern software development industry. AI-powered coding assistants such as GitHub Copilot, Claude Code, and other Large Language Model (LLM)-based systems are helping developers automate repetitive coding tasks, improve productivity, and accelerate software development processes. These tools can generate code, assist with debugging, provide recommendations, and support developers during implementation. However, despite their growing capabilities, many AI coding assistants still face challenges related to reliability, maintainability, project-specific conventions, and structured software engineering workflows. Most coding assistants perform well for generic programming tasks but often struggle when working with domain-specific development requirements, API integrations, project architectures, validation workflows, and coding standards. In real-world software engineering environments, developers require systems that not only generate code but also follow project conventions, maintain readability, support modular development, and improve long-term maintainability. The project “AI Agents Optimization” focuses on improving the reliability and effectiveness of AI coding agents by designing structured workflows, modular configurations, validation mechanisms, and optimized task execution strategies. The objective of the project is to investigate how AI agents can become dependable collaborators in practical software engineering tasks instead of functioning only as autocomplete systems. The project explores different approaches for organizing AI agent workflows using structured instruction handling, modular task division, context management, validation systems, and integration of external tools and documentation sources. Different agent configurations are analyzed and evaluated to understand how workflow optimization affects software development quality and performance. Why Existing AI Coding Workflows Often Fail Most AI coding assistants perform well for isolated coding tasks but struggle in real-world engineering environments where projects involve multiple files, coding standards, APIs, validation requirements, and contextual dependencies. For example, a generic prompt such as: “Build authentication middleware” may generate functional code, but the output often lacks: Project-specific architecture Error handling consistency Validation logic Security best practices Dependency awareness This project approaches the problem differently by introducing a structured workflow pipeline where AI agents operate in defined stages rather than generating outputs in a single step. The workflow separates planning, generation, validation, and refinement into independent modules. This improves maintainability, reduces inconsistent outputs, and supports iterative refinement similar to real software engineering workflows. Project Objectives The primary objective of this project is to optimize AI coding agents for real-world software engineering workflows. The project aims to improve how AI systems handle development tasks such as code generation, debugging, testing, validation, feature implementation, and workflow management. Another major objective is to design modular AI workflows where different stages of software development are managed systematically. The workflow focuses on task planning, instruction processing, validation, refinement, and output evaluation. This structured approach improves transparency, maintainability, and consistency in AI-generated outputs. The project also aims to evaluate how AI coding agents perform under different configurations and development scenarios. By testing multiple workflows and structured instruction methods, the project analyzes how optimization techniques improve development reliability and coding quality. Technologies and Tools Used The project utilizes multiple modern technologies and development tools for experimentation and workflow optimization. Technology / Tool Purpose Python Automation and scripting GitHub Copilot AI-assisted coding Claude / LLM APIs AI workflow experimentation Visual Studio Code Development environment Git & GitHub Version control and repository management Structured Prompting Workflow optimization MCP Concepts Tool and context integration These tools collectively support the implementation and testing of optimized AI coding workflows. Implementation Workflow The system was implemented using a modular AI workflow pipeline where each stage performs a dedicated engineering task. Step 1 — Task Parsing The user submits a development task or coding requirement. The Instruction Processing Module extracts: Objective Constraints Project context Expected output format Example structured prompt: Task: Create JWT authentication middleware Language: Node.js Constraints: - Use Express.js - Add token validation - Follow modular architecture - Include error handling Step 2 — Planning & Reasoning The Planning Module divides the task into subtasks such as: Route handling Token verification Error management Security validation This improves reasoning consistency before generation begins. Step 3 — Code Generation The Code Generation Module produces outputs using structured prompts and contextual references instead of generic instructions. Step 4 — Validation Generated outputs are validated using: Syntax checks Logical consistency checks Formatting standards Dependency validation Step 5 — Refinement If validation fails, the workflow loops back into refinement where issues are corrected before final delivery. System Workflow The workflow of the AI Agents Optimization system is based on modular task execution and structured development processes. The workflow begins with task planning and requirement analysis. The AI agent receives structured instructions along with coding constraints, project context, and validation requirements. The system processes the provided instructions and generates outputs according to defined workflows and development standards. Different configurations are tested to evaluate how instruction structures and modular task handling influence the quality of generated code The workflow also includes validation and refinement stages where generated outputs are analyzed for correctness, maintainability, and consistency. The project focuses not only on code generation but also on improving readability, workflow transparency, debugging support, and adherence to project conventions. Key Features of the Project Structured AI workflow design Modular task execution AI-assisted software development Workflow optimization strategies Validation and refinement mechanisms Integration of development tools and documentation Improved maintainability and readability Support for practical software engineering workflows Challenges Faced During Development One of the major challenges encountered during the project was maintaining consistency and reliability in AI-generated outputs. Different AI models often produce different responses depending on prompts, context, and task structure. Designing workflows that improve output stability and maintain coding standards required careful experimentation and optimization. Another challenge involved integrating structured workflows while ensuring flexibility in task execution. AI systems often require clear instructions and contextual information to produce accurate outputs. Balancing automation with maintainability and project-specific requirements was an important aspect of the project. Managing validation and refinement processes was also challenging because generated outputs needed to be evaluated not only for correctness but also for readability, maintainability, and software engineering best practices. Observations and Outcomes During experimentation, structured workflows produced more reliable and maintainable outputs compared to single-prompt generation approaches. Some important observations included: Reduced repetitive corrections during code refinement Improved consistency in generated outputs Better adherence to coding structure and formatting More stable workflow behavior for multi-step tasks Improved readability and maintainability of generated code The validation and refinement stages were particularly effective in reducing incomplete outputs and improving response quality. Although the project focuses primarily on workflow architecture and qualitative analysis rather than benchmark testing, the results demonstrate that modular AI pipelines can significantly improve practical software engineering workflows. Future Enhancements The project can be further enhanced by implementing advanced multi-agent collaboration systems where multiple AI agents work together on complex software development tasks. Future versions may also include real-time documentation integration, automated testing frameworks, cloud-based workflow management, and improved reasoning models. Additional enhancements may include IDE extensions, intelligent debugging systems, automated code review mechanisms, and adaptive workflow optimization based on project requirements. Conclusion The AI Agents Optimization project demonstrates how structured workflows and modular configurations can improve the effectiveness of AI-powered coding assistants in modern software engineering environments. By focusing on workflow optimization, validation mechanisms, modular task execution, and structured instruction handling, the project highlights the future potential of AI agents as reliable development collaborators capable of supporting real-world software engineering processes. The project represents an important step toward building dependable AI-assisted development systems that improve productivity, maintainability, and software quality while supporting modern engineering practices. How to Try This Workflow Define a structured development task Provide project constraints and context Break the task into subtasks Generate output using structured prompts Validate output quality Refine based on validation feedbackUnleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration
Artificial Intelligence is evolving rapidly, and one of the most pressing challenges is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) solves this problem by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, accessibility, and AI reasoning capabilities. What is Model Context Protocol (MCP)? MCP is a protocol designed to enable AI models, such as Azure OpenAI models, to interact seamlessly with external tools and services. Think of MCP as a universal USB-C connector for AI, allowing language models to fetch information, interact with APIs, and execute tasks beyond their built-in knowledge. Key Features of MCP Standardized Communication – MCP provides a structured way for AI models to interact with various tools. Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights. Secure & Scalable – Enables safe and scalable integration with enterprise applications. Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods. MCP Architecture & How It Works MCP follows a client-server architecture that allows AI models to interact with external tools efficiently. Here’s how it works: Components of MCP MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.). Data Sources – Various backend systems, including local storage, cloud databases, and external APIs. Data Flow in MCP The AI model sends a request (e.g., "fetch user profile data"). The MCP client forwards the request to the appropriate MCP server. The MCP server retrieves the required data from a database or API. The response is sent back to the AI model via the MCP client. Integrating MCP with Azure OpenAI Services Microsoft has integrated MCP with Azure OpenAI Services, allowing GPT models to interact with external services and fetch live data. This means AI models are no longer limited to static knowledge but can access real-time information. Benefits of Azure OpenAI Services + MCP Integration ✔ Real-time Data Fetching – AI assistants can retrieve fresh information from APIs, databases, and internal systems. ✔ Contextual AI Responses – Enhances AI responses by providing accurate, up-to-date information. ✔ Enterprise-Ready – Secure and scalable for business applications, including finance, healthcare, and retail. Hands-On Tools for MCP Implementation To implement MCP effectively, Microsoft provides two powerful tools: Semantic Workbench and AI Gateway. Microsoft Semantic Workbench A development environment for prototyping AI-powered assistants and integrating MCP-based functionalities. Features: Build and test multi-agent AI assistants. Configure settings and interactions between AI models and external tools. Supports GitHub Codespaces for cloud-based development. Explore Semantic Workbench Workbench interface examples Microsoft AI Gateway A plug-and-play interface that allows developers to experiment with MCP using Azure API Management. Features: Credential Manager – Securely handle API credentials. Live Experimentation – Test AI model interactions with external tools. Pre-built Labs – Hands-on learning for developers. Explore AI Gateway Setting Up MCP with Azure OpenAI Services Step 1: Create a Virtual Environment First, create a virtual environment using Python: python -m venv .venv Activate the environment: # Windows venv\Scripts\activate # MacOS/Linux source .venv/bin/activate Step 2: Install Required Libraries Create a requirements.txt file and add the following dependencies: langchain-mcp-adapters langgraph langchain-openai Then, install the required libraries: pip install -r requirements.txt Step 3: Set Up OpenAI API Key Ensure you have your OpenAI API key set up: # Windows setx OPENAI_API_KEY "<your_api_key> # MacOS/Linux export OPENAI_API_KEY=<your_api_key> Building an MCP Server This server performs basic mathematical operations like addition and multiplication. Create the Server File First, create a new Python file: touch math_server.py Then, implement the server: from mcp.server.fastmcp import FastMCP # Initialize the server mcp = FastMCP("Math") MCP.tool() def add(a: int, b: int) -> int: return a + b MCP.tool() def multiply(a: int, b: int) -> int: return a * b if __name__ == "__main__": mcp.run(transport="stdio") Your MCP server is now ready to run. Building an MCP Client This client connects to the MCP server and interacts with it. Create the Client File First, create a new file: touch client.py Then, implement the client: import asyncio from mcp import ClientSession, StdioServerParameters from langchain_openai import ChatOpenAI from mcp.client.stdio import stdio_client # Define server parameters server_params = StdioServerParameters( command="python", args=["math_server.py"], ) # Define the model model = ChatOpenAI(model="gpt-4o") async def run_agent(): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() tools = await load_mcp_tools(session) agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (4 + 6) x 14?"}) return agent_response["messages"][3].content if __name__ == "__main__": result = asyncio.run(run_agent()) print(result) Your client is now set up and ready to interact with the MCP server. Running the MCP Server and Client Step 1: Start the MCP Server Open a terminal and run: python math_server.py This starts the MCP server, making it available for client connections. Step 2: Run the MCP Client In another terminal, run: python client.py Expected Output 140 This means the AI agent correctly computed (4 + 6) x 14 using both the MCP server and GPT-4o. Conclusion Integrating MCP with Azure OpenAI Services enables AI applications to securely interact with external tools, enhancing functionality beyond text-based responses. With standardized communication and improved AI capabilities, developers can build smarter and more interactive AI-powered solutions. By following this guide, you can set up an MCP server and client, unlocking the full potential of AI with structured external interactions. Next Steps: Explore more MCP tools and integrations. Extend your MCP setup to work with additional APIs. Deploy your solution in a cloud environment for broader accessibility. For further details, visit the GitHub repository for MCP integration examples and best practices. MCP GitHub Repository MCP Documentation Semantic Workbench AI Gateway MCP Video Walkthrough MCP Blog MCP Github End to End DemoUnderstanding Azure OpenAI Service Quotas and Limits: A Beginner-Friendly Guide
Azure OpenAI Service allows developers, researchers, and students to integrate powerful AI models like GPT-4, GPT-3.5, and DALL·E into their applications. But with great power comes great responsibility and limits. Before you dive into building your next AI-powered solution, it's crucial to understand how quotas and limits work in the Azure OpenAI ecosystem. This guide is designed to help students and beginners easily understand the concept of quotas, limits, and how to manage them effectively. What Are Quotas and Limits? Think of Azure's quotas as your "AI data pack." It defines how much you can use the service. Meanwhile, limits are hard boundaries set by Azure to ensure fair use and system stability. Quota The maximum number of resources (e.g., tokens, requests) allocated to your Azure subscription. Limit The technical cap imposed by Azure on specific resources (e.g., number of files, deployments). Key Metrics: TPM & RPM Tokens Per Minute (TPM) TPM refers to how many tokens you can use per minute across all your requests in each region. A token is a chunk of text. For example, the word "Hello" is 1 token, but "Understanding" might be 2 tokens. Each model has its own default TPM. Example: GPT-4 might allow 240,000 tokens per minute. You can split this quota across multiple deployments. Requests Per Minute (RPM) RPM defines how many API requests you can make every minute. For instance, GPT-3.5-turbo might allow 350 RPM. DALL·E image generation models might allow 6 RPM. Deployment, File, and Training Limits Here are some standard limits imposed on your OpenAI resource: Resource Type Limit Standard model deployments 32 Fine-tuned model deployments 5 Training jobs 100 total per resource (1 active at a time) Fine-tuning files 50 files (total size: 1 GB) Max prompt tokens per request Varies by model (e.g., 4096 tokens for GPT-3.5) How to View and Manage Your Quota Step-by-Step: Go to the Azure Portal. Navigate to your Azure OpenAI resource. Click on "Usage + quotas" in the left-hand menu. You will see TPM, RPM, and current usage status. To Request More Quota: In the same "Usage + quotas" panel, click on "Request quota increase". Fill in the form: Select the region. Choose the model family (e.g., GPT-4, GPT-3.5). Enter the desired TPM and RPM values. Submit and wait for Azure to review and approve. What is Dynamic Quota? Sometimes, Azure gives you extra quota based on demand and availability. “Dynamic quota” is not guaranteed and may increase or decrease. Useful for short-term spikes but should not be relied on for production apps. Example: During weekends, your GPT-3.5 TPM may temporarily increase if there's less traffic in your region. Best Practices for Students Monitor Regularly: Use the Azure Portal to keep an eye on your usage. Batch Requests: Combine multiple tasks in one API call to save tokens. Start Small: Begin with GPT-3.5 before requesting GPT-4 access. Plan Ahead: If you're preparing a demo or a project, request quota in advance. Handle Limits Gracefully: Code should manage 429 Too Many Requests errors. Quick Resources Azure OpenAI Quotas and Limits How to Request Quota in Azure Join the Conversation on Azure AI Foundry Discussions! Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 🧠✨ 👉 Click here to join the discussion!Getting Started with the AI Toolkit: A Beginner’s Guide with Demos and Resources
If you're curious about building AI solutions but don’t know where to start, Microsoft’s AI Toolkit is a great place to begin. Whether you’re a student, developer, or just someone exploring AI for the first time, this toolkit helps you build real-world solutions using Microsoft’s powerful AI services. In this blog, I’ll Walk you through what the AI Toolkit is, how you can get started, and where you can find helpful demos and ready-to-use code samples. What is the AI Toolkit? The AI Toolkit is a collection of tools, templates, and sample apps that make it easier to build AI-powered applications and copilots using Microsoft Azure. With the AI Toolkit, you can: Build intelligent apps without needing deep AI expertise. Use templates and guides that show you how everything works. Quickly prototype and deploy apps with natural language, speech, search, and more. Watch the AI Toolkit in Action Microsoft has created a video playlist that covers the AI Toolkit and shows you how to build apps step-by-step. You can watch the full playlist here: It is especially useful for developers who want to bring AI into their projects, but also for beginners who want to learn by doing. AI Toolkit Playlist – https://aka.ms/AIToolkit/videos These videos help you understand the flow of building AI agents, using Azure OpenAI, and other cognitive services in a hands-on way. Explore Sample Projects on GitHub Microsoft also provides a public GitHub repository where you can find real code examples built using the AI Toolkit. Here’s the GitHub repo: AI Toolkit Samples – https://github.com/Azure-Samples/AI_Toolkit_Samples This repository includes: Sample apps using Azure AI services like OpenAI, Cognitive Search, and Speech. Instructions to deploy apps using Azure. Code that you can clone, test, and build on top of. You don’t have to start from scratch just open the code, understand the structure, and make small edits to experiment. How to Get Started Here’s a simple path if you’re just starting: Watch 2 or 3 videos from the AI Toolkit Playlist. Go to the GitHub repository and try running one of the examples. Make small changes to the code (like updating the prompt or output). Try deploying the solution on Azure by following the guide in the repo. Keep building and learning. Why This Toolkit is Worth Exploring As someone who is also learning and experimenting, I found this toolkit to be: Easy to understand, even for beginners. Focused on real-world applications, not just theory. Helpful for building responsible AI solutions with good documentation. It gives a complete picture — from writing code to deploying apps. Final Thoughts The AI Toolkit helps you start your journey in AI without feeling overwhelmed. It provides real code, real use cases, and practical demos. With the support of Microsoft Learn and Azure samples, you can go from learning to building in no time. If you’re serious about building with AI, this is a resource worth exploring. Continue the discussion in the Azure AI Foundry Discord community at Https://aka.ms/AI/discord Join the Azure AI Foundry Discord Server! References AI Toolkit Playlist (YouTube) https://aka.ms/AIToolkit/videos AI Toolkit GitHub Repository https://github.com/Azure-Samples/AI_Toolkit_Samples Microsoft Learn: AI Toolkit Documentation https://learn.microsoft.com/en-us/azure/ai-services/toolkit/ Azure AI Services https://azure.microsoft.com/en-us/products/ai-services/Model Mondays S2E11: Exploring Speech AI in Azure AI Foundry
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: Lakuna — Copilot Studio Agent for Product Teams: A hackathon project built with Copilot Studio and Azure AI Foundry, Lakuna analyzes your requirements and docs to surface hidden assumptions, helping teams reflect, test, and reduce bias in product planning. Azure ND H200 v5 VMs for AI: Azure Machine Learning introduced ND H200 v5 VMs, featuring NVIDIA H200 GPUs (over 1TB GPU memory per VM!) for massive models, bigger context windows, and ultra-fast throughput. Agent Factory Blog Series: The next wave of agentic AI is about extensibility: plug your agents into hundreds of APIs and services using Model Connector Protocol (MCP) for portable, reusable tool integrations. GPT-5 Tool Calling on Azure AI Foundry: GPT-5 models now support free-form tool calling—no more rigid JSON! Output SQL, Python, configs, and more in your preferred format for natural, flexible workflows. Microsoft a Leader in 2025 Gartner Magic Quadrant: Azure was again named a leader for Cloud Native Application Platforms—validating its end-to-end runway for AI, microservices, DevOps, and more. 2. Spotlight On: Azure AI Foundry Speech Playground The main segment featured a live demo of the new Azure AI Speech Playground (now part of Foundry), showing how developers can experiment with and deploy cutting-edge voice, transcription, and avatar capabilities. Key Features & Demos: Speech Recognition (Speech-to-Text): Try real-time transcription directly in the playground—recognizing natural speech, pauses, accents, and domain terms. Batch and Fast transcription options for large files and blob storage. Custom Speech: Fine-tune models for your industry, vocabulary, and noise conditions. Text to Speech (TTS): Instantly convert text into natural, expressive audio in 150+ languages with 600+ neural voices. Demo: Listen to pre-built voices, explore whispering, cheerful, angry, and more styles. Custom Neural Voice: Clone and train your own professional or personal voice (with strict Responsible AI controls). Avatars & Video Translation: Bring your apps to life with prebuilt avatars and video translation, which syncs voice-overs to speakers in multilingual videos. Voice Live API: Voice Live API (Preview) integrates all premium speech capabilities with large language models, enabling real-time, proactive voice agents and chatbots. Demo: Language learning agent with voice, avatars, and proactive engagement. One-click code export for deployment in your IDE. 3. Customer Story: Hilo Health This week’s customer spotlight featured Helo Health—a healthcare technology company using Azure AI to boost efficiency for doctors, staff, and patients. How Hilo Uses Azure AI: Document Management: Automates fax/document filing, splits multi-page faxes by patient, reduces staff effort and errors using Azure Computer Vision and Document Intelligence. Ambient Listening: Ambient clinical note transcription captures doctor-patient conversations and summarizes them for easy EHR documentation. Genie AI Contact Center: Agentic voice assistants handle patient calls, book appointments, answer billing/refill questions, escalate to humans, and assist human agents—using Azure Communication Services, Azure Functions, FastAPI (community), and Azure OpenAI. Conversational Campaigns: Outbound reminders, procedure preps, and follow-ups all handled by voice AI—freeing up human staff. Impact: Hilo reaches 16,000+ physician practices and 180,000 providers, automates millions of communications, and processes $2B+ in payments annually—demonstrating how multimodal AI transforms patient journeys from first call to post-visit care. 4. Key Takeaways Here’s what you need to know from S2E11: Speech AI is Accessible: The Azure AI Foundry Speech Playground makes experimenting with voice recognition, TTS, and avatars easy for everyone. From Playground to Production: Fine-tune, export code, and deploy speech models in your own apps with Azure Speech Service. Responsible AI Built-In: Custom Neural Voice and avatars require application and approval, ensuring ethical, secure use. Agentic AI Everywhere: Voice Live API brings real-time, multimodal voice agents to any workflow. Healthcare Example: Hilo’s use of Azure AI shows the real-world impact of speech and agentic AI, from patient intake to after-visit care. Join the Community: Keep learning and building—join the Discord and Forum. Sharda's Tips: How I Wrote This Blog I organize key moments from each episode, highlight product demos and customer stories, and use GitHub Copilot for structure. For this recap, I tested the Speech Playground myself, explored the docs, and summarized answers to common developer questions on security, dialects, and deployment. Here’s my favorite Copilot prompt this week: "Generate a technical blog post for Model Mondays S2E11 based on the transcript and episode details. Focus on Azure Speech Playground, TTS, avatars, Voice Live API, and healthcare use cases. Add practical links for developers and students!" Coming Up Next Week Next week: Observability! Learn how to monitor, evaluate, and debug your AI models and workflows using Azure and OpenAI tools. Register For The Livestream – Sep 1, 2025 Register For The AMA – Sep 5, 2025 Ask Questions & View Recaps – Discussion Forum About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Register For Livestreams Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Model Mondays S2E12: Models & Observability
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: GPT Real Time (GA): Azure AI Foundry now offers GPT Real Time (GA)—lifelike voices, improved instruction following, audio fidelity, and function calling, with support for image context and lower pricing. Read the announcement and check out the model card for more details. Azure AI Translator API (Public Preview): Choose between fast Neural Machine Translation (NMT) or nuanced LLM-powered translations, with real-time flexibility for multilingual workflows. Read the announcement then check out the Azure AI Translator documentation for more details. Azure AI Foundry Agents Learning Plan: Build agents with autonomous goal pursuit, memory, collaboration, and deep fine-tuning (SFT, RFT, DPO) - on Azure AI Foundry. Read the announcement what Agentic AI involves - then follow this comprehensive learning plan with step-by-step guidance. CalcLM Agent Grid (Azure AI Foundry Labs): Project CalcLM: Agent Grid is a prototype and open-source experiment that illustrates how agents might live in a grid-like surface (like Excel). It's formula-first and lightweight - defining agentic workflows like calculations. Try the prototype and visit Foundry Labs to learn more. Agent Factory Blog: Observability in Agentic AI: Agentic AI tools and workflows are gaining rapid adoption in the enterprise. But delivering safe, reliable and performant agents requires foundation support for Observability. Read the 6-part Agent Factory series and check out the Top 5 agent observability best practices for reliable AI blog post for more details. 2. Spotlight On: Observability in Azure AI Foundry This week’s spotlight featured a deep dive and demo by Han Che (Senior PM, Core AI/ Microsoft ), showing observability end-to-end for agent workflows. Why Observability? Ensures AI quality, performance, and safety throughout the development lifecycle. Enables monitoring, root cause analysis, optimization, and governance for agents and models. Key Features & Demos: Development Lifecycle: Leaderboard: Pick the best model for your agent with real-time evaluation. Playground: Chat and prototype agents, view instant quality and safety metrics. Evaluators: Assess quality, risk, safety, intent resolution, tool accuracy, code vulnerability, and custom metrics. Governance: Integrate with partners like Cradle AI and SideDot for policy mapping and evidence archiving. Red Teaming Agent: Automatically test for vulnerabilities and unsafe behavior. CI/CD Integration: Automate evaluation in GitHub Actions and Azure DevOps pipelines. Azure DevOps GitHub Actions Monitoring Dashboard: Resource usage, application analytics, input/output tokens, request latency, cost breakdown, and evaluation scores. Azure Cost Management SDKs & Local Evaluation: Run evaluations locally or in the cloud with the Azure AI Evaluation SDK. Demo Highlights: Chat with a travel planning agent, view run metrics and tool usage. Drill into run details, debugging, and real-time safety/quality scores. Configure and run large-scale agent evaluations in CI/CD pipelines. Compare agents, review statistical analysis, and monitor in production dashboards 3. Customer Story: Saifr Saifr is a RegTech company that uses artificial intelligence to streamline compliance for marketing, communications, and creative teams in regulated industries. Incubated at Fidelity Labs (Fidelity Investments’ innovation arm), Saifr helps enterprises create, review, and approve content that meets regulatory standards—faster and with less manual effort. What Saifr Offers AI-Powered Compliance: Saifr’s platform leverages proprietary AI models trained on decades of regulatory expertise to automatically detect potential compliance risks in text, images, audio, and video. Automated Guardrails: The solution flags risky or non-compliant language, suggests compliant alternatives, and provides explanations—all in real time. Workflow Integration: Saifr seamlessly integrates with enterprise content creation and approval workflows, including cloud platforms and agentic AI systems like Azure AI Foundry. Multimodal Support: Goes beyond text to check images, videos, and audio for compliance risks, supporting modern marketing and communications teams. 4. Key Takeaways Observability is Essential: Azure AI Foundry offers complete monitoring, evaluation, tracing, and governance for agentic AI—making production safe, reliable, and compliant. Built-In Evaluation and Red Teaming: Use leaderboards, evaluators, and red teaming agents to assess and continuously improve model safety and quality. CI/CD and Dashboard Integration: Automate evaluations in GitHub Actions or Azure DevOps, then monitor and optimize agents in production with detailed dashboards. Compliance Made Easy: Safer’s agents and models help financial services and regulated industries proactively meet compliance standards for content and communications. Sharda's Tips: How I Wrote This Blog I focus on organizing highlights, summarizing customer stories, and linking to official Microsoft docs and real working resources. For this recap, I explored the Azure AI Foundry Observability docs, tested CI/CD pipeline integration, and watched the customer demo to share best practices for regulated industries. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E12 based on the transcript and episode details. Focus on observability, agent dashboards, CI/CD, compliance, and customer stories. Add correct, working Microsoft links!" Coming Up Next Week Next week: Open Source Models! Join us for the final episode with Hugging Face VP of Product, live demos, and open model workflows. Register For The Livestream – Sep 15, 2025 About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Model Mondays S2E13: Open Source Models (Hugging Face)
1. Weekly Highlights 1. Weekly Highlights Here are the key updates we covered in the Season 2 finale: O1 Mini Reinforcement Fine-Tuning (GA): Fine-tune models with as few as ~100 samples using built-in Python code graders. Azure Live Interpreter API (Preview): Real-time speech-to-speech translation supporting 76 input languages and 143 locales with near human-level latency. Agent Factory – Part 5: Connecting agents using open standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent protocol). Ask Ralph by Ralph Lauren: A retail example of agentic AI for conversational styling assistance, built on Azure OpenAI and Foundry’s agentic toolset. VS Code August Release: Brings auto-model selection, stronger safety guards for sensitive edits, and improved agent workflows through new agents.md support. 2. Spotlight – Open Source Models in Azure AI Foundry Guest: Jeff Boudier, VP of Product at Hugging Face Jeff showcased the deep integration between the Hugging Face community and Azure AI Foundry, where developers can access over 10 000 open-source models across multiple modalities—LLMs, speech recognition, computer vision, and even specialized domains like protein modeling and robotics. Demo Highlights Discover models through Azure AI Foundry’s task-based catalog filters. Deploy directly from Hugging Face Hub to Azure with one-click deployment. Explore Use Cases such as multilingual speech recognition and vision-language-action models for robotics. Jeff also highlighted notable models, including: SmoLM3 – a 3 B-parameter model with hybrid reasoning capabilities Qwen 3 Coder – a mixture-of-experts model optimized for coding tasks Parakeet ASR – multilingual speech recognition Microsoft Research protein-modeling collection MAGMA – a vision-language-action model for robotics Integration extends beyond deployment to programmatic access through the Azure CLI and Python SDKs, plus local development via new VS Code extensions. 3. Customer Story – DraftWise (BUILD 2025 Segment) The finale featured a customer spotlight on DraftWise, where CEO James Ding shared how the company accelerates contract drafting with Azure AI Foundry. Problem Legal contract drafting is time-consuming and error-prone. Solution DraftWise uses Azure AI Foundry to fine-tune Hugging Face language models on legal data, generating contract drafts and redline suggestions. Impact Faster drafting cycles and higher consistency Easy model management and deployment with Foundry’s secure workflows Transparent evaluation for legal compliance 4. Community Story – Hugging Face & Microsoft The episode also celebrated the ongoing collaboration between Hugging Face and Microsoft and the impact of open-source AI on the global developer ecosystem. Community Benefits Access to State-of-the-Art Models without licensing barriers Transparent Performance through public leaderboards and benchmarks Rapid Innovation as improvements and bug fixes spread quickly Education & Empowerment via tutorials, docs, and active forums Responsible AI Practices encouraged through community oversight 5. Key Takeaways Open Source AI Is Here to Stay Azure AI Foundry and Hugging Face make deploying, fine-tuning, and benchmarking open models easier than ever. Community Drives Innovation: Collaboration accelerates progress, improves transparency, and makes AI accessible to everyone. Responsible AI and Transparency: Open-source models come with clear documentation, licensing, and community-driven best practices. Easy Deployment & Customization: Azure AI Foundry lets you deploy, automate, and customize open models from a single, unified platform. Learn, Build, Share: The open-model ecosystem is a great place for students, developers, and researchers to learn, build, and share their work. Sharda's Tips: How I Wrote This Blog For this final recap, I focused on capturing the energy of the open source AI movement and the practical impact of Hugging Face and Azure AI Foundry collaboration. I watched the livestream, took notes on the demos and interviews, and linked directly to official resources for models, docs, and community sites. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E13 based on the transcript and episode details. Focus on open source models, Hugging Face, Azure AI Foundry, and community workflows. Include practical links and actionable insights for developers and students! Learn & Connect Explore Open Models in Azure AI Foundry Hugging Face Leaderboard Responsible AI in Azure Machine Learning Llama-3 by Meta Hugging Face Community Azure AI Documentation About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Edge AI for Beginners : Getting Started with Foundry Local
In Module 08 of the EdgeAI for Beginners course, Microsoft introduces Foundry Local a toolkit that helps you deploy and test Small Language Models (SLMs) completely offline. In this blog, I’ll share how I installed Foundry Local, ran the Phi-3.5-mini model on my windows laptop, and what I learned through the process. What Is Foundry Local? Foundry Local allows developers to run AI models locally on their own hardware. It supports text generation, summarization, and code completion — all without sending data to the cloud. Unlike cloud-based systems, everything happens on your computer, so your data never leaves your device. Prerequisites Before starting, make sure you have: Windows 10 or 11 Python 3.10 or newer Git Internet connection (for the first-time model download) Foundry Local installed Step 1 — Verify Installation After installing Foundry Local, open Command Prompt and type: foundry --version If you see a version number, Foundry Local is installed correctly. Step 2 — Start the Service Start the Foundry Local service using: foundry service start You should see a confirmation message that the service is running. Step 3 — List Available Models To view the models supported by your system, run: foundry model list You’ll get a list of locally available SLMs. Here’s what I saw on my machine: Note: Model availability depends on your device’s hardware. For most laptops, phi-3.5-mini works smoothly on CPU. Step 4 — Run the Phi-3.5 Model Now let’s start chatting with the model: foundry model run phi-3.5-mini-instruct-generic-cpu:1 Once it loads, you’ll enter an interactive chat mode. Try a simple prompt: Hello! What can you do? The model replies instantly — right from your laptop, no cloud needed. To exit, type: /exit How It Works Foundry Local loads the model weights from your device and performs inference locally.This means text generation happens using your CPU (or GPU, if available). The result: complete privacy, no internet dependency, and instant responses. Benefits for Students For students beginning their journey in AI, Foundry Local offers several key advantages: No need for high-end GPUs or expensive cloud subscriptions. Easy setup for experimenting with multiple models. Perfect for class assignments, AI workshops, and offline learning sessions. Promotes a deeper understanding of model behavior by allowing step-by-step local interaction. These factors make Foundry Local a practical choice for learning environments, especially in universities and research institutions where accessibility and affordability are important. Why Use Foundry Local Running models locally offers several practical benefits compared to using AI Foundry in the cloud. With Foundry Local, you do not need an internet connection, and all computations happen on your personal machine. This makes it faster for small models and more private since your data never leaves your device. In contrast, AI Foundry runs entirely on the cloud, requiring internet access and charging based on usage. For students and developers, Foundry Local is ideal for quick experiments, offline testing, and understanding how models behave in real-time. On the other hand, AI Foundry is better suited for large-scale or production-level scenarios where models need to be deployed at scale. In summary, Foundry Local provides a flexible and affordable environment for hands-on learning, especially when working with smaller models such as Phi-3, Qwen2.5, or TinyLlama. It allows you to experiment freely, learn efficiently, and better understand the fundamentals of Edge AI development. Optional: Restart Later Next time you open your laptop, you don’t have to reinstall anything. Just run these two commands again: foundry service start foundry model run phi-3.5-mini-instruct-generic-cpu:1 What I Learned Following the EdgeAI for Beginners Study Guide helped me understand: How edge AI applications work How small models like Phi 3.5 can run on a local machine How to test prompts and build chat apps with zero cloud usage Conclusion Running the Phi-3.5-mini model locally with Foundry Localgave me hands-on insight into edge AI. It’s an easy, private, and cost-free way to explore generative AI development. If you’re new to Edge AI, start with the EdgeAI for Beginners course and follow its Study Guide to get comfortable with local inference and small language models. Resources: EdgeAI for Beginners GitHub Repo Foundry Local Official Site Phi Model LinkAzure AI Model Inference API
The Azure AI Model Inference API provides a unified interface for developers to interact with various foundational models deployed in Azure AI Studio. This API allows developers to generate predictions from multiple models without changing their underlying code. By providing a consistent set of capabilities, the API simplifies the process of integrating and switching between different models, enabling seamless model selection based on task requirements.
