Foundry Toolbox Lifecycle: Deploy and Promote
Foundry Toolbox Lifecycle: Deploy and Promote For developers looking to manage Microsoft Foundry tools across agents, this article presents one recommended pattern: define each Toolbox in its own azure.yaml , deploy it separately from project infrastructure and agents, and pass environment-specific Bicep outputs through GitHub Actions. Why Manage a Foundry Toolbox Separately? Organizations are integrating agent development into established software development lifecycles, raising questions about how teams govern and reuse tools. In this post, a “tool” means an external capability an agent invokes such as web search, a tool exposed by a Model Context Protocol (MCP) server, or a third-party application. Tools let agents retrieve live data or take actions in external systems. How can teams avoid rebuilding the same integrations? Microsoft Foundry Toolbox offers a shared way to manage tools. This post explores how to structure Toolbox ownership and deployment so a reusable collection doesn’t become a Toolshed. What Is a Microsoft Foundry Toolbox? A Toolbox in Foundry is a versioned collection of tools exposed through a single MCP-compatible endpoint. It is created in the context of a Foundry Project, and agents connect to that endpoint to use its tools. Microsoft’s Toolbox overview describes a way to define shared tools once and expose them through a single MCP-compatible endpoint. It centralizes tool access, credentials, governance, and versioning so teams can reuse approved tools across agents without rebuilding each integration. Instead of embedding a separate Web IQ integration in every Finance agent, teams can configure Web IQ once in a shared Toolbox. Each agent then connects to the Toolbox’s MCP endpoint and invokes the tools it exposes. The Toolbox and its project connections provide the shared configuration and credentials. Should a Toolbox Be Deployed with an Agent? Foundry examples show toolboxes created through SDKs and consumed by agents. The key design decision is which deployment lifecycle owns toolbox version changes. When multiple agents share a toolbox, manage its versions separately from any one agent’s deployment. A Foundry Project can contain zero or more agents. Agent deployments should be idempotent and managed independently. An agent’s availability should not depend on another agent. A Toolbox has its own lifecycle and versioning. A Toolbox lives outside any single agent, within a Foundry Project. Creating a toolbox with the Python SDK does not, by itself, make the toolbox part of an agent; the SDK call creates a version in the Foundry Project. The coupling arises when the agent’s deployment pipeline also owns and publishes that toolbox. To keep the lifecycles independent, manage toolbox versions through a project-level pipeline and have agents consume the toolbox endpoint. This avoids redeploying an unrelated agent for toolbox changes and avoids making shared tool availability depend on one agent’s lifecycle. Provision the Project, Then Deploy the Toolbox A Foundry project is provisioned as an Azure resource (`Microsoft.CognitiveServices/accounts/projects`), while toolbox versions are created through the Foundry project API. In this deployment pattern, we need to provision the project and any required connections first, then create and deploy the toolbox using the project endpoint. See the toolbox version API reference for details. The Foundry project and its project-level role-based access control (RBAC) and connections are infrastructure prerequisites that can be managed with infrastructure as code (IaC). The toolbox version is then created in a separate deployment step using the project endpoint, with environment-specific values supplied as Bicep outputs. This keeps the Toolbox alongside the project’s lifecycle without coupling it to any one agent. Where to Store the Toolbox Configuration Like the Foundry Project, a Toolbox is shared across agents and should follow the project’s lifecycle rather than an individual agent’s lifecycle. Store its definition alongside the project’s IaC and include its deployment in the project-level infrastructure workflow. Create a separate folder and `azure.yaml` for each Toolbox. This gives each Toolbox its own deployment step and `azd` environment, so teams can deploy, update, and version Toolboxes independently without coupling their release cycles. This post uses that structure to make each Toolbox’s lifecycle explicit. Foundry Toolbox Versioning and Promotion Foundry stores each Toolbox version as an immutable snapshot of its configuration. Changes create a new version rather than modifying an existing one. The first version becomes the default automatically; subsequent versions must be promoted explicitly. See Foundry Toolbox version managementfor details. Agents using the Toolbox’s stable consumer endpoint receive the default version. Developers can use a version specific endpoint to validate changes before promotion. Promoting a version changes what the consumer endpoint serves without requiring a rebuild and deploy of the agents code. To roll back, promote a previously retained version. Foundry provides these versioning and promotion capabilities, but the team still decides how changes are tested, approved, retained, and retired. A version captures Toolbox configuration not a frozen copy of the external services it calls—so rolling back the Toolbox does not necessarily reverse downstream service changes. i.e. changes that were made to tools of a custom downstream MCP server. Teams can remove older or unwanted versions after confirming they aren’t needed for rollback or used through version specific endpoints. If multiple versions exist, promote another version before deleting the current default. When using `azd`, deleting the only remaining version also removes the Toolbox. One important connection to our example: its `postdeploy` hook promotes immediately. We should distinguish that sample behavior from a production process that validates a candidate version before promotion. Deploy a Foundry Toolbox with GitHub Actions In this example, the Toolbox is deployed as a separate step in the same GitHub Actions workflow, after the Bicep deployment provisions the Foundry Project. This lets the workflow populate the Toolbox’s `azd` environment with the infrastructure outputs before deployment. You can also split the deployments across jobs or workflows, provided the Toolbox step receives the required outputs and runs after its prerequisites are ready. Prerequisites Foundry Project and region: Create the Foundry Project and any project connections required by the Toolbox before deployment. Confirm that the target region supports the Toolbox’s tool types and models using the tool support by region and model table Identity and access: Configure GitHub Actions to authenticate to Azure using OpenID Connect (OIDC), as in the linked sample. The identity that creates or updates Toolbox versions needs the Foundry User role on the project (Toolbox prerequisites). If the same workflow deploys Bicep, that identity also needs permission to deploy the template’s resources and create its role assignments at the applicable scopes. Tooling: Install Azure Developer CLI 1.37.0+, as required by the unified `microsoft.foundry` extension. Install the extension with `azd ext install microsoft.foundry`, following the Foundry extension installation guidance. The linked sample pins `azd` 1.37.1 and installs `azure.ai.toolboxes` separately. Configuration Provide the project endpoint, environment name, location, and every downstream endpoint or connection name referenced by the Toolbox’s `azure.yaml`. Create the required Foundry project connections before deploying the Toolbox. Configure credentials on those connections or in an approved secret store, not in Bicep outputs; see Toolbox authentication. - name: Deploy and promote the toolbox working-directory: foundation/toolboxes/fibey-toolbox env: AZURE_ENV_NAME: ${{ steps.infrastructure.outputs.AZURE_ENV_NAME }} FOUNDRY_PROJECT_ENDPOINT: ${{ steps.infrastructure.outputs.FOUNDRY_PROJECT_ENDPOINT }} AZURE_AI_PROJECT_ENDPOINT: ${{ steps.infrastructure.outputs.AZURE_AI_PROJECT_ENDPOINT }} run: | azd deploy fibey-toolbox --no-prompt The infrastructure deployment should expose the values the Toolbox needs as outputs—in this case, Bicep outputs. The linked sample uses `AZURE_ENV_NAME`, `AZURE_LOCATION`, `FOUNDRY_PROJECT_ENDPOINT`, and `AZURE_AI_PROJECT_ENDPOINT` for the deployment context. Its Toolbox configuration also consumes `INVENTORY_MCP_URL`, `WORK_ORDERS_API_URL`, `KB_MCP_URL`, and `KB_CONNECTION_NAME`. Expose the corresponding values for each target environment and region. The Toolbox’s `azure.yaml` references the imported values, so the same definition can be reused across environments and regions. For each target, provide that target’s infrastructure outputs instead of manually changing endpoint values, and confirm that the required services and tools are supported in the target region. The example references `steps.infrastructure.outputs` because the infrastructure and Toolbox deployment steps run in the same GitHub Actions job. If they run in separate jobs, expose the required values as job outputs and consume them through `needs`. This keeps the deployments connected without tying their release cycles together: infrastructure provides the deployment context, and the Toolbox consumes it. Keep credentials in managed identities or approved secret stores rather than ordinary deployment outputs. The complete workflow demonstrates authentication, infrastructure outputs, and environment initialization. Its extension-install step currently uses `azure.ai.toolboxes`; replace that step with `azd ext install microsoft.foundry` when following the current Learn guidance above. Version promotion is handled by the `postdeploy` hook in the Toolbox’s `azure.yaml`. Verify Toolbox Deployment and Promotion The sample’s `postdeploy` hook promotes the deployed version to the default and checks that promotion succeeded. Redeploying an unchanged Toolbox definition does not create a new version. To inspect the versions and confirm which one is default, run: azd ai toolbox versions list fibey-toolbox The default version is marked with `*`. This confirms which version is default, but not that its tools work. For runtime verification, follow the Toolbox verification guidance to initialize MCP and list tools using the version-specific endpoint. Test a tool call only with a safe, read-only operation. The developer needs the Foundry User role on the project; prompt-agent and OAuth scenarios can require additional permissions for the agent identity or end user. Troubleshooting If deployment or promotion fails, check that the deployment identity has the required project role, that the project endpoint and Toolbox configuration values were supplied, and that the required project connections and tools are available in the target region. Conclusion Toolbox provides a shared way to manage tools across agents without tying their lifecycle to any one agent. Keeping toolbox definitions alongside your Foundry Project infrastructure, while deploying them as a separate step, lets teams update and version tools independently while retaining a clear dependency on the project. As your organization adopts this pattern, consider who owns each toolbox, how changes are tested and promoted, and who can access its tools. That governance will help keep a useful Toolbox from becoming a Toolshed.The GitHub app in Microsoft Teams is now generally available!
GitHub empowers any team to become builders in Microsoft Teams. Customer support teams are using GitHub to turn customer feedback from Teams channels into filed issues. Engineering teams are asking GitHub to investigate, reproduce, and fix bugs without leaving Teams. Business and marketing teams with innovative ideas are asking GitHub to turn their discussions into working prototypes. During public preview, our team has been hard at work addressing feedback and making product improvements to the GitHub experience in Teams. Here is what’s new. Review work in the GitHub Canvas The new GitHub canvas lets you review the plan, file changes, and pull request without leaving Teams. Click “Open Canvas” to follow GitHub Copilot’s progress and see your team’s ideas take shape in code, right beside the conversation. Go from notification to pull request in one app GitHub notifications and GitHub Copilot now live in a single app in Teams called GitHub. Stay on top of issue and pull request updates and act on them right in Teams. When an update needs attention, reply to the notification with "@GitHub". GitHub Copilot can investigate, make the fix, and draft a pull request in the same thread, so your team sees the update and the response together. Issues and pull requests created from Teams now carry attribution. Reviewers can see who started the work and link back to the Teams conversation where it was discussed. Work across repositories and tools Real work rarely lives in one repository. With multi-repo support, GitHub Copilot can work across all the repositories your task needs in the same conversation. In addition, MCP support lets you configure which tools and connected services GitHub can leverage in Teams. Learn more about configuring MCP. Get set up in a guided experience The new guided web onboarding experience helps you sign in through your default browser using saved passwords or passkeys and grant Teams access to the right organizations and repositories. You can now use natural language to ask GitHub to use a default repository or switch repositories in your Teams conversations. You can also choose the model GitHub Copilot uses for a task. Pick a faster model for quick fixes or a more capable model for larger changes. Get started today Add the GitHub app to Teams today, then click “Get started” from its welcome message to connect your account. Type "@GitHub help" to see what else it can do. Admins should verify the following are enabled for users to get started: Microsoft 365 admins: ensure resource-specific consent is enabled so users can let the GitHub app use conversation context. TAC admins: ensure the GitHub app is allowed for users in the Teams admin center. GitHub enterprise admins: ensure Copilot cloud agent, cloud sandboxes, and CLI billing are enabled for each organization and assign GitHub Copilot licenses to users. Learn more at teams.github.com! We'd love to hear what you build. Share your feedback in the comments below.Turn GitHub notifications into code with GitHub Copilot in Microsoft Teams
GitHub can now bring repository updates into a channel, group chat, or private chat. When an update needs action, reply to the notification and @mention GitHub with the task you want GitHub Copilot to carry out. GitHub Copilot uses the issue or pull request linked in the notification and the conversation in the thread to understand the task. It can reproduce a bug, run tests, implement a fix, and prepare a pull request for review. Turn an issue notification into a pull request If the player doesn’t load, open the video in a new window: Open video For a bug report, the workflow starts in the notification thread: A GitHub Issue notification arrives in a Teams channel with a link to the reported bug. You @mention GitHub to investigate the issue and prepare a proposed fix in a pull request. The team reviews the pull request, iterates with GitHub Copilot, tests the proposed changes, and decides whether to merge. Starting from the linked issue and thread lets your team delegate the work to GitHub Copilot without rebuilding the request from scratch. The same workflow can be used to implement a feature, expand test coverage, update documentation, and more. Product and engineering teams at Microsoft are already leveraging the GitHub app in Teams to build products today. Get started Explore the new GitHub app in Microsoft Teams (previously called GitHub Notifications). If you already have GitHub Notifications installed, update the app to the latest version. The legacy GitHub app will be renamed “GitHub (deprecated)” and retired on November 16. Learn more at teams.github.com! Turn the updates your team follows into code your team can review.Build an AI-Powered Space Invaders Game
Build an AI-Powered Space Invaders Game: Integrating LLMs into HTML5 Games with Microsoft Foundry Local Introduction What if your game could talk back to you? Imagine playing Space Invaders while an AI commander taunts you during battle, delivers personalized mission briefings, and provides real-time feedback based on your performance. This isn't science fiction it's something you can build today using HTML, JavaScript, and a locally-running AI model. In this tutorial, we'll explore how to create an HTML5 game with integrated Large Language Model (LLM) features using Microsoft Foundry Local. You'll learn how to combine classic game development with modern AI capabilities, all running entirely on your own machine—no cloud services, no API costs, no internet connection required during gameplay. We'll be working with the Space Invaders - AI Commander Edition project, which demonstrates exactly how to architect games that leverage local AI. Whether you're a student learning game development, exploring AI integration patterns, or building your portfolio, this guide provides practical, hands-on experience with technologies that are reshaping how we build interactive applications. What You'll Learn By the end of this tutorial, you'll understand how to combine traditional web development with local AI inference. These skills transfer directly to building chatbots, interactive tutorials, AI-enhanced productivity tools, and any application where you want intelligent, context-aware responses. Set up Microsoft Foundry Local for running AI models on your machine Understand the architecture of games that integrate LLM features Use GitHub Copilot CLI to accelerate your development workflow Implement AI-powered game features like dynamic commentary and adaptive feedback Extend the project with your own creative AI features Why Local AI for Games? Before diving into the code, let's understand why running AI locally matters for game development. Traditional cloud-based AI services have limitations that make them impractical for real-time gaming experiences. Latency is the first challenge. Cloud API calls typically take 500ms to several seconds, an eternity in a game running at 60 frames per second. Local inference can respond in tens of milliseconds, enabling AI responses that feel instantaneous and natural. When an enemy ship appears, your AI commander can taunt you immediately, not three seconds later. Cost is another consideration. Cloud AI services charge per token, which adds up quickly when generating dynamic content during gameplay. Local models have zero per-use cost, once installed, they run entirely on your hardware. This frees you to experiment without worrying about API bills. Privacy and offline capability complete the picture. Local AI keeps all data on your machine, perfect for games that might handle player information. And since nothing requires internet connectivity, your game works anywhere, on planes, in areas with poor connectivity, or simply when you want to play without network access. Understanding Microsoft Foundry Local Microsoft Foundry Local is a runtime that enables you to run small language models (SLMs) directly on your computer. It's designed for developers who want to integrate AI capabilities into applications without requiring cloud infrastructure. Think of it as having a miniature AI assistant living on your laptop. Foundry Local handles the complex work of loading AI models, managing memory, and processing inference requests through a simple API. You send text prompts, and it returns AI-generated responses, all happening locally on your CPU or GPU. The models are optimized to run efficiently on consumer hardware, so you don't need a supercomputer. For our Space Invaders game, Foundry Local powers the "AI Commander" feature. During gameplay, the game sends context about what's happening, your score, accuracy, current level, enemies remaining and receives back contextual commentary, taunts, and encouragement. The result feels like playing alongside an AI companion who actually understands the game. Setting Up Your Development Environment Let's get your machine ready for AI-powered game development. We'll install Foundry Local, clone the project, and verify everything works. The entire setup takes about 10-15 minutes. Step 1: Install Microsoft Foundry Local Foundry Local installation varies by operating system. Open your terminal and run the appropriate command: # Windows (using winget) winget install Microsoft.FoundryLocal # macOS (using Homebrew) brew install microsoft/foundrylocal/foundrylocal These commands download and install the Foundry Local runtime along with a default small language model. The installation includes everything needed to run AI inference locally. Verify the installation by running: foundry --version If you see a version number, Foundry Local is ready. If you encounter errors, ensure you have administrator/sudo privileges and that your package manager is up to date. Step 2: Install Node.js (If Not Already Installed) Our game's AI features require a small Node.js server to communicate between the browser and Foundry Local. Check if Node.js is installed: node --version If you see a version number (v16 or higher recommended), you're set. Otherwise, install Node.js: # Windows winget install OpenJS.NodeJS.LTS # macOS brew install node # Linux sudo apt install nodejs npm Node.js provides the JavaScript runtime that powers our proxy server, bridging browser code with the local AI model. Step 3: Clone the Project Get the Space Invaders project onto your machine: git clone https://github.com/leestott/Spaceinvaders-FoundryLocal.git cd Spaceinvaders-FoundryLocal This downloads all game files, including the HTML interface, game logic, AI integration module, and server code. Step 4: Install Dependencies and Start the Server Install the Node.js packages and launch the AI-enabled server: npm install npm start The first command downloads required packages (primarily for the proxy server). The second starts the server, which listens for AI requests from the game. You should see output indicating the server is running on port 3001. Step 5: Play the Game Open your browser and navigate to: http://localhost:3001 You should see Space Invaders with "AI: ONLINE" displayed in the game HUD, indicating that AI features are active. Use arrow keys or A/D to move, SPACE to fire, and P to pause. The AI Commander will start providing commentary as you play! Understanding the Project Architecture Now that the game is running, let's explore how the different pieces fit together. Understanding this architecture will help you modify the game and apply these patterns to your own projects. The project follows a clean separation of concerns, with each file handling a specific responsibility: Spaceinvaders-FoundryLocal/ ├── index.html # Main game page and UI structure ├── styles.css # Retro arcade visual styling ├── game.js # Core game logic and rendering ├── llm.js # AI integration module ├── sound.js # Web Audio API sound effects ├── server.js # Node.js proxy for Foundry Local └── package.json # Project configuration index.html: Defines the game canvas and UI elements. It's the entry point that loads all other modules. game.js: Contains the game loop, physics, collision detection, scoring, and rendering logic. This is the heart of the game. llm.js: Handles all communication with the AI backend. It formats game state into prompts and processes AI responses. server.js: A lightweight Express server that proxies requests between the browser and Foundry Local. sound.js: Synthesizes retro sound effects using the Web Audio API—no audio files needed! How the AI Integration Works The magic of the AI Commander happens through a simple but powerful pattern. Let's trace the flow from gameplay event to AI response. When something interesting happens in the game, you clear a wave, achieve a combo, or lose a life, the game logic in game.js triggers an AI request. This request includes context about the current game state: your score, accuracy percentage, current level, lives remaining, and what just happened. The llm.js module formats this context into a prompt. For example, when you clear a wave with 85% accuracy, it might construct: You are an AI Commander in a Space Invaders game. The player just cleared wave 3 with 85% accuracy. Score: 12,500. Lives: 3. Provide a brief, enthusiastic comment (1-2 sentences). This prompt travels to server.js , which forwards it to Foundry Local. The AI model processes the prompt and generates a response like: "Impressive accuracy, pilot! Wave 3 didn't stand a chance. Keep that trigger finger sharp!" The response flows back through the server to the browser, where llm.js passes it to the game. The game displays the message in the HUD, creating the illusion of playing alongside an AI companion. This entire round trip typically completes in 50-200 milliseconds, fast enough to feel responsive without interrupting gameplay. Using GitHub Copilot CLI to Explore and Modify the Code GitHub Copilot CLI accelerates your development workflow by letting you ask questions and generate code directly in your terminal. Let's use it to understand and extend the Space Invaders project. Installing Copilot CLI If you haven't installed Copilot CLI yet, here's the quick setup: # Install GitHub CLI winget install GitHub.cli # Windows brew install gh # macOS # Authenticate with GitHub gh auth login # Add Copilot extension gh extension install github/gh-copilot # Verify installation gh copilot --help With Copilot CLI ready, you can interact with AI directly from your terminal while working on the project. Exploring Code with Copilot CLI Use Copilot to understand unfamiliar code. Navigate to the project directory and try: gh copilot explain "How does llm.js communicate with the server?" Copilot analyzes the code and explains the communication pattern, helping you understand the architecture without reading every line manually. You can also ask about specific functions: gh copilot explain "What does the generateEnemyTaunt function do?" This accelerates onboarding to unfamiliar codebases, a valuable skill when working with open source projects or joining teams. Generating New Features Want to add a new AI feature? Ask Copilot to help generate the code: gh copilot suggest "Create a function that asks the AI to generate a mission briefing at the start of each level, including the level number and a random mission objective" Copilot generates starter code that you can customize and integrate. This combination of AI-powered development tools and AI-integrated gameplay demonstrates how LLMs are transforming both how we build games and how games behave. Customizing the AI Commander The default AI Commander provides generic gaming commentary, but you can customize its personality and responses. Open llm.js to find the prompt templates that control AI behavior. Changing the AI's Personality The system prompt defines who the AI "is." Find the base prompt and modify it: // Original const systemPrompt = "You are an AI Commander in a Space Invaders game."; // Customized - Drill Sergeant personality const systemPrompt = `You are Sergeant Blaster, a gruff but encouraging drill sergeant commanding space cadets. Use military terminology, call the player "cadet," and be tough but fair.`; // Customized - Supportive Coach personality const systemPrompt = `You are Coach Nova, a supportive and enthusiastic gaming coach. Use encouraging language, celebrate small victories, and provide gentle guidance when players struggle.`; These personality changes dramatically alter the game's feel without changing any gameplay code. It's a powerful example of how AI can add variety to games with minimal development effort. Adding New Commentary Triggers Currently the AI responds to wave completions and game events. You can add new triggers in game.js : // Add AI commentary when player achieves a kill streak if (killStreak >= 5 && !streakCommentPending) { requestAIComment('killStreak', { count: killStreak }); streakCommentPending = true; } // Add AI reaction when player narrowly avoids death if (nearMissOccurred) { requestAIComment('nearMiss', { livesRemaining: lives }); } Each new trigger point adds another opportunity for the AI to engage with the player, making the experience more dynamic and personalized. Understanding the Game Features Beyond AI integration, the Space Invaders project demonstrates solid game development patterns worth studying. Let's explore the key features. Power-Up System The game includes eight different power-ups, each with unique effects: SPREAD (Orange): Fires three projectiles in a spread pattern LASER (Red): Powerful beam with high damage RAPID (Yellow): Dramatically increased fire rate MISSILE (Purple): Homing projectiles that track enemies SHIELD (Blue): Grants an extra life EXTRA LIFE (Green): Grants two extra lives BOMB (Red): Destroys all enemies on screen BONUS (Gold): Random score bonus between 250-750 points Power-ups demonstrate state management, tracking which power-up is active, applying its effects to player actions, and handling timeouts. Study the power-up code in game.js to understand how temporary state modifications work. Leaderboard System The game persists high scores using the browser's localStorage API: // Saving scores localStorage.setItem('spaceInvadersScores', JSON.stringify(scores)); // Loading scores const savedScores = localStorage.getItem('spaceInvadersScores'); const scores = savedScores ? JSON.parse(savedScores) : []; This pattern works for any data you want to persist between sessions—game progress, user preferences, or accumulated statistics. It's a simple but powerful technique for web games. Sound Synthesis Rather than loading audio files, the game synthesizes retro sound effects using the Web Audio API in sound.js . This approach has several benefits: no external assets to load, smaller project size, and complete control over sound parameters. Examine how oscillators and gain nodes combine to create laser sounds, explosions, and victory fanfares. This knowledge transfers directly to any web project requiring audio feedback. Extending the Project: Ideas for Students Ready to make the project your own? Here are ideas ranging from beginner-friendly to challenging, each teaching valuable skills. Beginner: Customize Visual Theme Modify styles.css to create a new visual theme. Try changing the color scheme from green to blue, or create a "sunset" theme with orange and purple gradients. This builds CSS skills while making the game feel fresh. Intermediate: Add New Enemy Types Create a new enemy class in game.js with different movement patterns. Perhaps enemies that move in sine waves, or boss enemies that take multiple hits. This teaches object-oriented programming and game physics. Intermediate: Expand AI Interactions Add new AI features like: Pre-game mission briefings that set up the story Dynamic difficulty hints when players struggle Post-game performance analysis and improvement suggestions AI-generated names for enemy waves Advanced: Multiplayer Commentary Modify the game for two-player support and have the AI provide play-by-play commentary comparing both players' performance. This combines game networking concepts with advanced AI prompting. Advanced: Voice Integration Use the Web Speech API to speak the AI Commander's responses aloud. This creates a more immersive experience and demonstrates browser speech synthesis capabilities. Troubleshooting Common Issues If something isn't working, here are solutions to common problems. "AI: OFFLINE" Displayed in Game This means the game can't connect to the AI server. Check that: The server is running ( npm start shows no errors) You're accessing the game via http://localhost:3001 , not directly opening the HTML file Foundry Local is installed correctly ( foundry --version works) Server Won't Start If npm start fails: Ensure you ran npm install first Check that port 3001 isn't already in use by another application Verify Node.js is installed ( node --version ) AI Responses Are Slow Local AI performance depends on your hardware. If responses feel sluggish: Close other resource-intensive applications Ensure your laptop is plugged in (battery mode may throttle CPU) Consider that first requests may be slower as the model loads Key Takeaways Local AI enables real-time game features: Microsoft Foundry Local provides fast, free, private AI inference perfect for gaming applications Clean architecture matters: Separating game logic, AI integration, and server code makes projects maintainable and extensible AI personality is prompt-driven: Changing a few lines of prompt text completely transforms how the AI interacts with players Copilot CLI accelerates learning: Use it to explore unfamiliar code and generate new features quickly The patterns transfer everywhere: Skills from this project apply to chatbots, assistants, educational tools, and any AI-integrated application Conclusion and Next Steps You've now seen how to integrate AI capabilities into a browser-based game using Microsoft Foundry Local. The Space Invaders project demonstrates that modern AI features don't require cloud services or complex infrastructure, they can run entirely on your laptop, responding in milliseconds. More importantly, you've learned patterns that extend far beyond gaming. The architecture of sending context to an AI, receiving generated responses, and integrating them into user experiences applies to countless applications: customer support bots, educational tutors, creative writing tools, and accessibility features. Your next step is experimentation. Clone the repository, modify the AI's personality, add new commentary triggers, or build an entirely new game using these patterns. The combination of GitHub Copilot CLI for development assistance and Foundry Local for runtime AI gives you powerful tools to bring intelligent applications to life. Start playing, start coding, and discover what you can create when your games can think. Resources Space Invaders - AI Commander Edition Repository - Full source code and documentation Play Space Invaders Online - Try the basic version without AI features Microsoft Foundry Local Documentation - Official installation and API guide GitHub Copilot CLI Documentation - Installation and usage guide GitHub Education - Free developer tools for students Web Audio API Documentation - Learn about browser sound synthesis Canvas API Documentation - Master HTML5 game renderingTurn conversations into code with GitHub Copilot in Microsoft Teams
Coding where context lives and collaboration happens The best prompt may be the conversation your team has already had or is currently having. Until now, using a coding agent often meant paying context and coordination taxes. Developers had to leave the discussion, open another tool, and reconstruct the problem in a lengthy prompt: what happened, what the team decided, which constraints matter, and what needs to be built. With GitHub Copilot in Teams, teams can move directly from conversation to action. @mention GitHub Copilot when the team is ready to act, and it can use the conversation alongside repository context to understand the request, implement the change, and create a pull request for review. This makes working with a coding agent more collaborative and visible. Instead of one developer privately reconstructing the request, teammates can contribute context, correct assumptions, refine the approach in real time, and review the resulting work together. Let’s explore the new GitHub Copilot in Teams experience to see how it can streamline development tasks: If the player doesn’t load, open the video in a new window: Open video GitHub Copilot in Teams is available in Teams channels, group chats, meeting chats, and 1:1 chats. Once the GitHub app is installed and added to the conversation, @-mention GitHub Copilot to bring it into the discussion and start a task. During public preview, users can complete the following scenarios with GitHub Copilot in Teams: Build a feature based on requirements discussed in Teams Implement a fix for a bug Expand test coverage or improve documentation Create and update a pull request No copying the discussion into a CLI. No rewriting it in a desktop app. No asking one developer to translate a team decision into the perfect prompt. GitHub Copilot works where the context already lives, and because that context is shared, working with GitHub Copilot becomes a team activity. Built around the controls teams already use GitHub Copilot in Teams works within existing GitHub permissions and repository policies. Branch protections and required reviews continue to apply, and people remain responsible for deciding what gets merged, ensuring that humans stay in the loop at every step. Availability GitHub Copilot in Teams is now available in Public Preview. To try it, install the GitHub app for Microsoft Teams and check out our documentation to get started! Less context reconstruction. More progress from the conversations already happening.Model router updates: new regions, a refreshed model pool, and understanding the hill climb
Across Microsoft, "hill climbing" has become shorthand for how real AI progress happens: not in one dramatic leap, but through a disciplined loop. Microsoft AI defines the hill climb as an organization that continuously improves, cycle after cycle, through more compute, better data, and sharper evaluation. Reinforcement fine-tuning in Foundry defines it as improving the deployable model package one measured step at a time across quality, latency, and cost. Different altitudes, same premise: progress is not a one-shot decision. It's a loop. For most teams, the decision of what model to use when is made manually or with custom routing tools. A developer picks a model based on benchmarks, familiarity, or the last launch that made headlines, ships it, and revisits the choice only when something breaks. In an ecosystem where the frontier moves monthly, that decision goes stale fast. Model router in Foundry Models brings the hill climb to the selection layer. What's new: a bigger pool, in more places This release expands where teams can deploy model router, broaden the supported model pool, and delivers updates through a stable endpoint. Together, these changes help teams run production workloads in more locations, match a wider range of tasks to suitable models, and adopt supported updates without changing the application integration. A refreshed model pool. The supported model list now includes Anthropic Claude Opus 4.8 — a high-capability model built for complex reasoning and long-form generation, for scenarios that demand depth, structure, and quality — and the GPT-5.6 family. Just as importantly, the pool is pruned: gpt-5-chat, gpt-5.2-chat, gpt-5.3-chat, Deepseek-V3.1 have been removed from the model router as models reach the end of their lifecycle and are deprecated in Foundry. New region availability. The model router is now available in 28 regions for global standard and 21 data zone regions. For many organizations, inference requests must stay within specific geographic boundaries for regulatory, governance, or customer-trust reasons — and intelligent routing shouldn't force a compromise on that. Find the full list of regions here. The most important detail is what you don't have to do: these updates occur automatically*. The endpoint remains stable as the supported model pool is refreshed, so teams do not need to redeploy the model router to receive the update. Applications can continue using the same integration while the model router evaluates requests against the current supported pool. Teams should continue monitoring routing traces and application outcomes to confirm that quality, cost, latency, and governance requirements are met. *Models from Anthropic still need to be deployed separately before they can be routed to through the model router. Interested in hearing more about what's new to the model router? Tune in for the next episode of Model Mondays with Sanjeev Jagtap and Lee Stott, where they talk all things model router from evaluations to hill climbing. Sign up here to watch live or view the replay: Model Mondays - Spotlight On Model router in Microsoft Foundry | Microsoft Reactor The selection-layer hill climb At the selection layer, a step is a routing decision. Each one is a micro-optimization against your objective, and each one is instrumented: every response from the model router includes a model field showing which underlying model was selected, so the climb leaves a complete, auditable trail. Model router supports three parts of the optimization loop: A/B testing to compare two router configurations to understand quality, cost, and latency tradeoffs; model decomposition to use routing results to decompose a single-model application into a multi-model or multi-agent design, and continuous routing to keep the router in production for continuous per-request selection. Each pattern turns model choice into a measured, repeatable process rather than a fixed decision. 1. A/B Testing Question: Which model or routing strategy should I use in production? A/B testing helps teams compare candidate models, model families, or router configurations against the same workload. Representative traffic is sent to competing deployments, and teams compare quality, cost, latency, and governance outcomes. The goal is to understand tradeoffs and identify the model or routing strategy that best meets workload requirements before promoting it to production. 2. Model Decomposition Question: What work is my application actually doing? Model decomposition uses model router as a diagnostic tool. By deploying the model router against a representative workload and examining routing telemetry, teams can see how requests naturally separate into different task classes. Simple retrieval, classification, and summarization requests may route to smaller models, while reasoning, planning, and agentic workflows may require more capable models. The goal is not to choose a winner, but to understand the structure of the workload and uncover opportunities for optimization, specialization, or architectural improvements. 3. Route continuously Question: Why choose a single model at all? Route continuously is the pattern model router was designed for but is not limited to. Rather than treating model selection as a one-time decision, teams leave the model router in production and allow the best-fit model to be selected for each request. As the supported model pool, regional availability, and platform capabilities evolve, teams can continue using the same endpoint while evaluating whether updates improve workload outcomes. Model selection becomes an ongoing optimization process rather than a project that must be repeated every time the model landscape changes. Together, these patterns illustrate a broader shift: the model router is more than a model. It is a tool for the optimization loop itself, helping teams evaluate tradeoffs, understand workload behavior, test hypotheses, and continuously refine model selection as requirements evolve. Whether used to compare candidate models, decompose applications into specialized tasks, or automate per-request routing in production, model router turns model selection into an observable, measurable, and repeatable process. As the model landscape continues to change, that optimization loop becomes a durable advantage. Getting Started Ready to start your own hill climb? Whether you're exploring the model router for the first time, evaluating routing strategies against your workload, or building a long-term optimization practice, these resources can help you move from experimentation to production with Microsoft Foundry. What's new in model router? Sign up for the next Model Mondays episode for a deep dive into new features, optimization patterns, and the latest model router updates. How do I build agents with model router? Check out the Model Router Agents Lab and build agent experiences with routing, retrieval, web search, tool calling, and multi-agent patterns. How do I evaluate model router? Compare model router against baseline models using your own prompts, then review quality, cost, latency, and routing decisions with the Auto Evaluation Toolkit. How do I optimize model router for my workload? Start your hill-climbing journey with the Model Mastery workshop, where you'll test one optimization lever at a time and measure how each change impacts workload outcomes. How do I build a model router optimization playbook? Explore the Model Releases repository to track new capabilities, understand the optimization question behind each release, and try focused notebooks that demonstrate one optimization lever at a time.Distributing Agents to Microsoft Teams and Microsoft 365 Copilot Part 4/5
This is the fourth post in our series on the Microsoft agent platform. We cover the Distribute in M365 pillar — publishing your agents to Microsoft Teams and Microsoft 365 Copilot so they reach users where they already work. All examples reference the FibreOps repository, demonstrated at Microsoft Build BRK241. The Distribution Story Building a great agent is only half the challenge. The other half is getting it into the hands of users without asking them to learn a new tool, visit a new URL, or change their workflow. Microsoft 365 Copilot and Microsoft Teams are where enterprise users already spend their day, making them the natural distribution surface for agents. With the GA release, publishing an agent to Teams and M365 Copilot is a single command. No separate app registration portal, no manual manifest assembly, no multi-step approval workflow for development and testing. Publishing to Microsoft 365 Copilot (GA) FibreOps ships as a declarative agent + action plugin ready for sideload. A single CLI command produces the complete package: python -m fibreops.demo publish-m365 --out dist/m365 # Output: # ✓ wrote dist/m365/declarativeAgent.json # ✓ wrote dist/m365/fibreops-action.json # ✓ wrote dist/m365/manifest.json # ✓ wrote dist/m365/color.png (192x192) # ✓ wrote dist/m365/outline.png ( 32x32) # ✓ wrote dist/m365/fibreops-copilot.zip What Gets Generated File Purpose declarativeAgent.json Defines the agent's persona, capabilities, and conversation starters for M365 Copilot fibreops-action.json Action plugin that proxies tool calls to the deployed FastAPI backend via OpenAPI manifest.json Teams app manifest with publisher metadata, permissions, and capabilities color.png / outline.png App icons for Teams and M365 surfaces fibreops-copilot.zip Ready-to-upload package for Teams Admin Center Configuration Set the base URL to your deployed FastAPI app before publishing — the action plugin uses this to resolve the OpenAPI runtime: # Set the public HTTPS hostname of the deployed FastAPI app $env:M365_ACTION_BASE_URL = "https://fibreops-demo.azurewebsites.net" # Optional: customise publisher metadata $env:M365_PUBLISHER_NAME = "Contoso Network Operations" $env:M365_PUBLISHER_WEBSITE = "https://contoso.com/noc" # Generate the package python -m fibreops.demo publish-m365 --out dist/m365 Environment Variable Purpose M365_ACTION_BASE_URL Public HTTPS root for the FastAPI /openapi.json (e.g., Container Apps FQDN) M365_APP_ID Override the generated Teams app GUID (default: deterministic per repo) M365_PUBLISHER_NAME Publisher name shown in M365 Admin Center M365_PUBLISHER_WEBSITE Publisher website link Uploading the Package Upload the generated fibreops-copilot.zip through either path: Teams Admin Center → Manage apps → Upload new app M365 Admin Center → Integrated apps → Upload custom apps Once uploaded, the declarative agent: Inherits the publisher metadata you configured Advertises conversation starters from the FibreOps deck (e.g., "What is the current outage status?", "Dispatch an engineer to FN-LDN-001") Proxies tool calls to the deployed FastAPI app via the action plugin Appears in Microsoft 365 Copilot as a specialised agent users can invoke How Declarative Agents Work A declarative agent in Microsoft 365 Copilot is defined by metadata rather than code running in the M365 surface. The intelligence lives in your backend — Copilot handles the conversational UX, tool orchestration schema, and user authentication. The flow: User invokes the agent in Microsoft 365 Copilot or Teams Copilot renders conversation starters and accepts natural language input When the agent needs to act, Copilot calls the action plugin (your OpenAPI endpoint) Your FastAPI backend processes the request using the full agent pipeline Results return to the user in the Copilot/Teams UX This architecture means your agent logic stays in one place — the backend. The M365 surface is purely a distribution and interaction layer. Action Plugins and OpenAPI The action plugin ( fibreops-action.json ) references your FastAPI app's /openapi.json endpoint. FibreOps exposes a JSON API that the action plugin can call: /api/runs — List and query agent runs /api/optimiser — Get optimizer scores and suggestions /sdk/chat — Natural language interaction with the agent system /healthz — Liveness probe Because FastAPI auto-generates OpenAPI schemas from your typed Python endpoints, the action plugin gets accurate parameter descriptions, response schemas, and error codes without any manual specification work. Publishing as Autopilots (Public Preview) Autopilots take distribution one step further — agents that operate autonomously without requiring a user to initiate each interaction. An Autopilot can: React to events (e.g., a critical telemetry signal) without human initiation Take actions within defined guardrails Notify users only when human intervention is needed Operate continuously across Microsoft 365 surfaces For FibreOps, an Autopilot would monitor the Event Hub stream continuously and only surface to the NOC team when an incident exceeds automated resolution capability — a fully autonomous operations agent. Teams Adaptive Cards FibreOps posts rich Adaptive Card notifications to Microsoft Teams throughout the agent pipeline. This is separate from the declarative agent — it is a push notification channel for real-time operational awareness. # The NetOps agent posts an outage notice via Incoming Webhook def post_outage_notice(incident_id, node_id, severity, summary, engineer=None): card = { "type": "AdaptiveCard", "body": [ {"type": "TextBlock", "text": f"🚨 Outage: {node_id}", "weight": "Bolder", "size": "Large"}, {"type": "FactSet", "facts": [ {"title": "Severity", "value": severity.upper()}, {"title": "Incident", "value": incident_id}, {"title": "Summary", "value": summary}, ]}, ], "actions": [ {"type": "Action.OpenUrl", "title": "View in NOC Console", "url": f"{base_url}/runs/{incident_id}"} ] } # POST to Teams webhook or append to outbox for offline mode ... If TEAMS_WEBHOOK_URL is not configured, cards are appended to state/teams_outbox.jsonl for review in the NOC console's Teams panel. End-to-End: From Code to Copilot Here is the complete flow from development to distribution: Build — Develop agents with Microsoft Agent Framework, test locally with python -m fibreops.demo --backend local Publish agents — python -m fibreops.demo publish creates hosted Prompt Agents in Foundry Deploy infrastructure — azd up provisions App Service, ACR, Event Hub, Key Vault, and Application Insights Deploy hosted agent — azd env set FIBREOPS_DEPLOY_HOSTED true && azd up Generate M365 package — python -m fibreops.demo publish-m365 --out dist/m365 Upload to Teams — Upload fibreops-copilot.zip via Teams Admin Center Users interact — The agent is now available in Microsoft 365 Copilot and Teams Security Considerations Managed Identity — The deployed app uses system-assigned managed identity for all Azure service access. No secrets in code. Least privilege — Each role grant is scoped to the minimum required (Event Hubs Data Owner, Key Vault Secrets User, AcrPull, Azure AI Developer). Authentication — The M365 Copilot surface handles user authentication; your backend receives authenticated requests. Guardrails — Autopilots operate within defined boundaries; human-in-the-loop escalation is built into the Routine and agent decision logic. Key Takeaways Publishing to Teams and M365 Copilot is GA — a single command generates the complete package. Declarative agents separate distribution (M365) from intelligence (your backend). Action plugins leverage your existing FastAPI OpenAPI schema — no manual specification needed. Autopilots (Public Preview) enable fully autonomous operation within guardrails. Adaptive Cards provide real-time push notifications alongside the conversational agent surface. The same backend serves the NOC console, the Copilot SDK, and the M365 declarative agent. Next Steps Explore the FibreOps repository — try python -m fibreops.demo publish-m365 Microsoft 365 Copilot extensibility documentation Next in this series: Voice Live and Observability for Production Agent SystemsBuilding Autonomous Agents with Microsoft Agent Framework and GitHub Copilot SDK Part 2/5
This is the second post in our series on the Microsoft agent platform. Here we dive deep into building autonomous agents, the development experience, the Microsoft Agent Framework, tool design patterns, and how the GitHub Copilot SDK brings conversational AI to your agent system. All examples reference the FibreOps repository, an autonomous fibre outage response system demonstrated at Microsoft Build BRK241. The Microsoft Agent Framework The Microsoft Agent Framework (now GA) provides a unified programming model for building agents. It supports multiple backends through a single .run() contract: Hosted — FoundryAgent connected to a Prompt Agent published to Microsoft Foundry Agent Service. Foundry — Agent + FoundryChatClient with the definition resolved locally (ideal for prompt iteration). Local — Deterministic LocalAgent for offline development and testing. This design means your orchestration code never changes regardless of where the agent runs. The factory pattern in FibreOps selects the backend at startup: # src/fibreops/agents/factory.py — simplified from agent_framework_foundry import FoundryAgent from agent_framework import Agent, FoundryChatClient def build_agent(role: str, backend: str, config: Config): if backend == "hosted": return FoundryAgent(agent_id=config.foundry_agents[role]) elif backend == "foundry": return Agent( instructions=get_instructions(role), chat_client=FoundryChatClient(endpoint=config.endpoint), tools=get_tools(role), ) else: return LocalAgent(role=role) Set FIBREOPS_AGENT_BACKEND to override the backend, or leave it as auto for intelligent detection. Designing Role-Specialised Agents FibreOps demonstrates a key pattern: role specialisation. Rather than one monolithic agent, the system uses three focused agents, each with a clear responsibility boundary: Agent Role Tools Available IncidentAnalysisAgent Classify severity, find root cause, retrieve SOP Knowledge (SOPs + topology), Web IQ, Work IQ NetOpsCoordinatorAgent File D365 incident, post Teams notice Ticketing, Teams, Memory FieldDispatchAgent Select engineer, book resource, update team Dispatch, Teams, Voice Why Role Specialisation? Focused system prompts — Each agent has a tightly scoped instruction set, reducing hallucination and improving reliability. Independent evaluation — You can score each agent separately against role-specific criteria. Parallel development — Teams can iterate on agents independently. Selective upgrade — Swap one agent's model or implementation without touching others. Tool Design: Typed Python Functions Tools in the Microsoft Agent Framework are typed Python functions that the runtime supplies to the hosted agent definition. FibreOps demonstrates several tool categories: Knowledge Tools # src/fibreops/tools/knowledge.py — simplified def sop_lookup(node_id: str, signal_type: str) -> dict: """Retrieve the Standard Operating Procedure for a given signal type. Args: node_id: The fibre node identifier (e.g., FN-LDN-001) signal_type: The type of signal (loss_of_light, high_ber, signal_degradation) Returns: SOP with steps, escalation path, and estimated resolution time. """ # Load from local markdown SOPs or Foundry IQ ... def web_iq_search(query: str, *, limit: int = 5) -> list[dict]: """Search public web for context relevant to the incident. Grounding against roadworks, weather, power outages, splice guidance. Falls back to deterministic fixtures when endpoint is unset. """ ... def work_iq_search(query: str, *, limit: int = 5) -> list[dict]: """Search enterprise knowledge for context relevant to the incident. Site surveys, SLA tiers, competency matrix, MTTR trends. """ ... Integration Tools # src/fibreops/tools/teams.py — simplified def post_outage_notice( incident_id: str, node_id: str, severity: str, summary: str, engineer: str | None = None, ) -> dict: """Post an Adaptive Card outage notice to the configured Teams channel. If TEAMS_WEBHOOK_URL is not set, appends to state/teams_outbox.jsonl for offline review. """ card = build_adaptive_card(incident_id, node_id, severity, summary, engineer) if config.teams_webhook_url: requests.post(config.teams_webhook_url, json=card) else: append_to_outbox(card) return {"status": "posted", "incident_id": incident_id} Design Principles for Agent Tools Typed parameters with docstrings — The runtime uses type hints and docstrings to generate the tool schema for the LLM. Graceful degradation — Every tool works offline by falling back to local fixtures or file-based state. Idempotent where possible — Tools that create resources return existing records if called with the same parameters. Observable — Every tool invocation emits an OpenTelemetry span for tracing and debugging. The Orchestrator Pattern The orchestrator drives signals through the agent pipeline. It is deliberately simple — a linear flow with error handling: # src/fibreops/orchestrator.py — simplified async def handle_signal(signal: TelemetrySignal) -> RunResult: """Process a telemetry signal through the agent pipeline.""" # Stage 1: Incident Analysis analysis = await incident_agent.run( f"Analyse this signal: {signal.model_dump_json()}" ) # Stage 2: NetOps Coordination coordination = await netops_agent.run( f"Coordinate response for: {analysis.summary}" ) # Stage 3: Field Dispatch dispatch = await dispatch_agent.run( f"Dispatch engineer for incident: {coordination.incident_id}" ) return RunResult( signal=signal, analysis=analysis, coordination=coordination, dispatch=dispatch, ) The orchestrator honours the same contract regardless of backend — hosted , foundry , or local — because all backends implement await agent.run(prompt) . GitHub Copilot SDK Integration (GA) The GitHub Copilot SDK enables conversational interaction with your agent system. FibreOps implements FibreOpsCopilotClient with the same interface as github/copilot-sdk : # src/fibreops/sdk/__init__.py — simplified from fibreops.sdk.client import FibreOpsCopilotClient client = FibreOpsCopilotClient() session = client.create_session() # Query agent status response = session.send_and_wait("status") print(response.text) # Human-readable summary print(response.data) # Structured JSON # Inject a telemetry signal via conversation response = session.send_and_wait(json.dumps({ "signal_id": "sig-demo", "node_id": "FN-LDN-001", "signal_type": "loss_of_light", "severity": "critical" })) The adapter routes prompts by shape: JSON signal-shaped dicts — Forwarded to the orchestrator for processing. Free-form text — Answered by a deterministic responder ( help , status , nodes , engineers , optimiser , dispatch ). Drive it from the terminal: python -m fibreops.demo chat "help" python -m fibreops.demo chat "status" python -m fibreops.demo chat '{"signal_id":"sig-demo","node_id":"FN-LDN-001","signal_type":"loss_of_light","severity":"critical"}' Or hit the embedded HTTP endpoint when the NOC console is running: Invoke-RestMethod -Method Post http://127.0.0.1:8800/sdk/chat -Body '{"prompt":"status"}' -ContentType application/json Development Workflow with Foundry Toolkit for VS Code The Foundry Toolkit for VS Code provides an integrated development experience: Author prompts — Edit system instructions with live preview and token counting. Test locally — Run against the foundry backend with FoundryChatClient pointing at your development model. Iterate fast — The foundry backend resolves definitions locally, so prompt changes take effect immediately without republishing. Publish when ready — python -m fibreops.demo publish creates hosted Prompt Agents in Foundry. Multi-Model Support The Microsoft Agent Framework supports multiple models. FibreOps defaults to gpt-4.1-mini (the model available in most demo Foundry accounts), but any chat-completions deployment works: # .env AZURE_AI_MODEL_DEPLOYMENT=gpt-4.1-mini # or gpt-4o-mini, gpt-4o, gpt-4.1 The framework also supports Claude Code connectors and Magentic-One for multi-agent collaboration scenarios. Testing Strategy FibreOps demonstrates a layered testing approach: Unit tests — Test tools in isolation with mocked dependencies. Local backend tests — Run the full pipeline with LocalAgent for deterministic assertions. Integration tests — Run against real Foundry agents with pytest -q . Rubric evaluation — The optimizer scores every run against defined criteria. # Run the test suite .\.venv\Scripts\python.exe -m pytest -q Key Takeaways The Microsoft Agent Framework provides a unified .run() contract across hosted, foundry, and local backends. Role specialisation keeps agents focused, testable, and independently evolvable. Tools are typed Python functions with docstrings — the runtime generates schemas automatically. The GitHub Copilot SDK (GA) enables conversational interaction with any agent system. Graceful degradation means the entire system works offline for development. The factory pattern lets you switch backends without changing orchestration code. Next Steps Clone the FibreOps repository and run python -m fibreops.demo --signals 3 Microsoft Agent Framework documentation Next in this series: Running Hosted Agents in Microsoft Foundry Agent ServiceCustomer Offerings: Solution Optimization for GitHub Copilot
Introduction As Microsoft Cloud Solution Architects, we are increasingly asked by engineering leaders, platform teams, finance stakeholders, and GitHub administrators how they can scale GitHub Copilot while keeping usage visible and costs predictable. The question is no longer simply how many licenses have been assigned. Agentic experiences, model choice, context size, reasoning level, and task complexity can all influence consumption. To address this challenge, Microsoft customers can engage their account team to request a Solution Optimization for GitHub Copilot engagement. The engagement is designed to help customers create visibility, establish practical cost guardrails, and improve the quality and efficiency of GitHub Copilot usage. The objective is not to minimize every token; it is to help each interaction produce useful outcomes with the right level of capability and control. What changed with GitHub Copilot billing? As of June 1, 2026, GitHub Copilot moved from premium request units to usage-based billing with GitHub AI Credits. When a user interacts with an AI-powered Copilot feature, the interaction can consume input tokens, output tokens, and cached tokens. The model used and the number of tokens processed determine the AI credit consumption, where one AI credit represents $0.01 USD. Area What customers should know Included usage Copilot Business includes 1,900 AI credits per licensed user per month, while Copilot Enterprise includes 3,900. These credits are pooled at the billing entity level. Promotional period At the time of writing, existing Business and Enterprise customers receive higher promotional allowances through August 2026. Validate the current allowance before publishing or making financial decisions. Included experiences Code completions and next edit suggestions remain included on paid plans and do not consume AI credits. Metered experiences AI-powered features such as Copilot Chat, Copilot CLI, Copilot cloud agent, Copilot Spaces, Spark, and supported third-party coding agents consume AI credits. Additional consumption Some agentic experiences, including Copilot code review and cloud agent scenarios, can also consume GitHub Actions minutes in addition to AI credits. Important: Pricing, allowances, supported models, and product behavior can change. The official GitHub documentation and the customer’s commercial agreement remain the source of truth. Why optimization is more than cost reduction Token consumption is only the visible part of the problem. A poorly scoped task, unnecessary context, or the wrong model can cause an agent to misunderstand the request, make excessive changes, or require repeated attempts. In that situation, reducing the price of an individual request does not solve the underlying quality problem. A better optimization strategy starts with agent quality. The right model, clear instructions, focused context, good repository guidance, and deterministic validation can help Copilot complete work in fewer attempts. Better outcomes and lower consumption frequently reinforce each other. Introducing Solution Optimization for GitHub Copilot The Solution Optimization for GitHub Copilot engagement provides customers with an opportunity to work with a Microsoft Cloud Solution Architect to review how GitHub Copilot is being adopted, consumed, governed, and measured. The final scope should be agreed with the Microsoft account team and may vary according to the customer’s licensing model, environment, maturity, and priorities. A typical engagement can focus on the following areas: Usage visibility: Establish a current-state view of adoption, AI credit consumption, model activity, license allocation, and the users or workflows driving demand. Billing readiness: Review exported usage data, compare scenarios, and identify where the move to usage-based billing changes planning assumptions. Cost guardrails: Design user-level, cost-center, organization, and enterprise budget controls that protect the shared pool without unnecessarily blocking productive users. Agent quality and token optimization: Identify improvements to model selection, prompt structure, context management, reasoning levels, repository instructions, and validation steps. Operating model: Define ownership, review cadence, escalation paths, reporting responsibilities, and a prioritized backlog of recommended actions. Potential customer outcomes Depending on the agreed scope, the engagement can help the customer develop: A baseline of GitHub Copilot adoption, AI credit consumption, and cost drivers. A view of heavy users, underused licenses, high-consumption models, and agentic workflows that need closer review. A budget and guardrail design that balances shared-pool flexibility with predictable financial control. Practical recommendations for improving agent quality and reducing avoidable retries or context overhead. A prioritized optimization plan with owners, next actions, and measurable follow-up points. A practical engagement approach Discover: Review the customer’s goals, GitHub billing entity, Copilot plans, license assignment model, existing policies, cost centers, and available usage data. Analyze: Use native GitHub reporting and approved accelerators to examine adoption, AI credit usage, model activity, user patterns, and potential cost drivers. Optimize: Map the right model and reasoning level to each task, improve prompts and context, reduce unnecessary tool or repository context, preserve reusable cache, and plan before executing complex changes. Govern: Define user-level budgets, power-user overrides, cost-center or organization controls, enterprise spending limits, alerts, and ownership responsibilities. Measure: Document the baseline, agree on key indicators, and establish a regular review cycle to measure adoption, value, quality, and consumption over time. Tools and accelerators GitHub AI usage, billing exports, and budget controls GitHub’s native AI usage pages and billing reports should be the starting point for understanding consumption. They provide the information required to identify model usage, users, features, and cost patterns. Native budget controls can then be applied at the appropriate user, cost-center, organization, or enterprise scope. GitHub AI usage dashboard, exported usage report, and budget controls GitHub Copilot Billing Preview The GitHub Copilot Billing Preview is an open-source web application for analyzing Copilot billing CSV reports, comparing request-based and usage-based billing signals, and exploring usage and cost trends by user, organization, model, product, and cost center. CSV processing occurs locally in the browser. The application is a preview and planning tool, not the billing source of record. GitHub Copilot Billing Preview showing usage and cost trends Copilot Insights dashboard The community-developed Copilot Insights dashboard provides centralized views of adoption, license allocation, AI credit consumption, model activity, productivity indicators, and team-level reporting. It can complement native GitHub data when a customer needs richer visualization or executive reporting. Customers should review the project’s security, deployment, support, and governance model before production use. Copilot Insights adoption, license, and AI credit views Agent quality and token optimization principles GitHub’s guidance emphasizes that the most sustainable way to reduce AI credit consumption is to improve the quality and efficiency of each interaction. The following principles provide a useful starting point: Choose the right model for the task. Reserve powerful reasoning models for complex architecture, debugging, and design work. Use mid-tier or lighter models for well-scoped implementation, documentation, formatting, and routine refactoring. Provide clear guidance. State the goal, constraints, expected output, relevant files, and validation criteria. Ambiguous prompts often lead to exploration and rework. Keep context lean. Supply the information needed for the task and avoid loading unrelated repositories, files, tools, or instructions into the context window. Preserve reusable context. Stable instructions and cached context can reduce repeated processing, provided they remain relevant and accurate. Research and plan before implementation. For complex work, separate discovery and planning from execution so the agent does not repeatedly rediscover the same information. Add deterministic guardrails. Tests, linting, build validation, explicit stop conditions, and session limits help prevent long-running or low-quality loops. Measure value, not only consumption. AI credit data should be considered together with adoption, developer experience, quality, delivery outcomes, and business impact. Overview video The on-demand session GitHub Copilot - Token Optimization [AMER/EMEA] explains the relationship between token usage and agent quality. The session covers how large language models, agent harnesses, context windows, and available controls influence agent behavior, quality, consumption, and cost. https://www.youtube.com/live/LeALSSsbzHU?si=4RgxflItkQFAs9Qk Helpful inputs before the engagement The following inputs can help the Microsoft team and customer make the best use of the engagement: A GitHub enterprise or organization owner, billing manager, and relevant engineering or platform stakeholders. Current Copilot plan and license counts, including the billing entity and cost-center structure. A representative GitHub AI usage or billing export, handled according to the customer’s data policies. Existing budgets, spending policies, model policies, and reporting processes. Examples of high-volume agent sessions, code review workflows, or teams reporting unexpected consumption. The business outcomes the customer wants to improve, such as adoption, developer experience, delivery speed, quality, or cost predictability. Conclusion GitHub Copilot usage-based billing changes the conversation from license assignment alone to the broader discipline of operating AI-assisted software development. Customers need visibility into how AI credits are consumed, controls that prevent unexpected spend, and engineering practices that help agents complete work accurately and efficiently. The Solution Optimization for GitHub Copilot engagement can help customers connect usage data to practical decisions, improve agent quality, establish appropriate financial guardrails, and create a repeatable approach for measuring and optimizing GitHub Copilot over time. How do I book this engagement? Microsoft Unified Support customers can contact their Customer Success Account Manager (CSAM) or Microsoft account team and ask about Solution Optimization for GitHub Copilot. The Microsoft team can confirm availability, eligibility, scope, prerequisites, and scheduling for the customer’s environment. Resources GitHub Copilot usage-based billing for organizations and enterprises Getting started with GitHub Copilot budget controls Optimizing AI usage to maximize efficiency and reduce cost Models and pricing for GitHub Copilot GitHub Copilot Billing Preview Copilot Insights dashboard GitHub Copilot - Token Optimization [AMER/EMEA] Disclaimer Pricing, plan entitlements, AI credit allowances, supported models, and product behavior are subject to change. Always verify current information in the official GitHub documentation and the customer’s commercial agreement before making purchasing, budgeting, or technical decisions. The sample applications, dashboards, and scripts referenced in this article are provided AS IS without warranty of any kind and may not be supported under a Microsoft or GitHub standard support program unless explicitly stated. Customers are responsible for reviewing security, privacy, compliance, deployment, and operational requirements before using community or open-source solutions in production. This blog post was drafted with the assistance of generative AI and should be reviewed and approved by the author and relevant Microsoft stakeholders before publication.
