From Requirement to Production Code, How Engineering Squad Automates the Full Dev Lifecycle
I started wondering: what if instead of one AI assistant generating code snippets, you had an entire squad of specialized AI agents. Each owning a single stage of the delivery pipeline, they could collaborate, self-correct, and produce a complete, traceable output from a plain-text requirement? That's Engineering Squad: an open-source, multi-agent framework built with LangGraph, Azure OpenAI, and Foundry Local. Nine agents. One pipeline. Zero manual handoffs. You give it a requirement. It gives you back: - User stories with acceptance criteria - Technical design (API contracts, data models, architecture) - Full implementation code (written into real files, not markdown) - Unit tests and Playwright E2E tests - Automated code review with a self-correcting feedback loop When the Code Reviewer finds a bug, it doesn't just flag it, it routes the work back to the exact agent that needs to fix it. When the Spec Agent hits ambiguity, it stops and asks you rather than guessing. The loop runs up to 5 iterations, and every run is versioned under a unique Run ID for full traceability. It runs on Azure OpenAI for heavy reasoning, Foundry Local for lightweight tasks or entirely offline with --local-only mode. No cloud required. How It Works The squad is a directed graph of 9 specialized agents. Each agent has a single responsibility and a tuned system prompt. The orchestration is handled by LangGraph's StateGraph, which routes work through the pipeline and handles feedback loops. The Agents Agent Model Responsibility Product Owner Azure OpenAI gpt-4.1 Reads requirements, classifies impact scope Story Agent Foundry Local (qwen2.5-7b) Converts requirements → structured user stories Spec Agent Azure OpenAI o3 Resolves ambiguity — asks the user interactively Technical Design Azure OpenAI gpt-4.1 Architecture, API contracts, data models, error handling Developer Azure OpenAI gpt-4.1 Writes code directly into the codebase Unit Tester Azure OpenAI gpt-4.1 Writes unit tests and evaluates them against implementation Test Writer Foundry Local (qwen2.5-7b) Writes Playwright E2E tests using Page Object Model Tester Azure OpenAI o3 Final evaluation of code against all specs and tests Code Reviewer Azure OpenAI o3 Reviews everything, decides: approve or route back The Self-Correcting Loop This is where it gets interesting. The Code Reviewer doesn't just say "approved" or "rejected" — it makes a routing decision using structured output: class ReviewDecision(BaseModel): decision: Literal[ "approved", # Ship it "requirement_confusion", # → Spec Agent (clarify ambiguity) "clarity_missing", # → Technical Design (refine design) "code_missing", # → Developer (fix implementation) "bug_found", # → Developer (fix bugs) "test_case_missing", # → Test Writer (add coverage) ] feedback: str # Actionable feedback for the target agent LangGraph's conditional edges route the workflow back to the exact agent that needs to act. The loop runs up to 5 iterations with a hard stop to prevent infinite cycles. workflow.add_conditional_edges( "code_reviewer", route_review, { END: END, "spec_agent": "spec_agent", "technical_design": "technical_design", "developer": "developer", "test_writer": "test_writer", }, ) Key Design Decisions 1. Impact Classification — Don't Run What You Don't Need Not every change needs the full pipeline. The squad classifies scope first: Scope What Runs config Impact Analysis → Developer → Unit Tester → Reviewer bugfix Impact Analysis → Developer → Unit Tester → Tester → Reviewer enhancement Stories → Design (if needed) → Developer → All Tests → Reviewer feature Stories → Design → Developer → All Tests → Reviewer refactor Impact Analysis → Developer → Unit Tester → Reviewer A config change doesn't need user stories. A bugfix doesn't need a full architectural design. This keeps runs fast and focused. 2. Code Goes Into Real Files, Not Markdown This was a deliberate choice. The Developer Agent edits actual source files in your project — it doesn't dump code into a markdown artifact. The code_changes.md artifact is a change log that records what was modified and why, for traceability. 3. Existing Projects vs. Greenfield Set PROJECT_TYPE: existing in requirements_input.txt, point it at your repos, and the squad will: Scan your codebase for patterns, conventions, and architecture Make targeted changes only — no rewriting from scratch Preserve your existing coding style, error handling, and naming conventions 4. Two LLM Tiers — Cloud + Local The framework uses a hybrid model strategy: Azure OpenAI (gpt-4.1, o3) for complex reasoning: code generation, technical design, code review Foundry Local (qwen2.5-7b, phi-3.5-mini) for lightweight tasks: user stories, test writing This keeps costs down while maintaining quality where it matters. And with --local-only mode, you can run the entire squad on Foundry Local with zero cloud dependencies. Running It Locally with Foundry Local One of my favorite features: the entire squad can run 100% locally using Foundry Local. No Azure subscription, no API keys, no internet required. Setup # Install Foundry Local CLI (one-time) winget install Microsoft.FoundryLocal # Install Python dependencies pip install foundry-local-sdk openai langchain-openai langgraph python-dotenv # Run in local-only mode python main.py --local-only When --local-only is set, every agent that would normally call Azure OpenAI gets redirected to Foundry Local: def get_azure_llm(deployment: str, temperature: float = 0.1): # Local-only mode: redirect to Foundry Local if os.getenv("SQUAD_LOCAL_ONLY", "").lower() in ("true", "1", "yes"): from models.local_llm import get_local_llm return get_local_llm(temperature=temperature) # Otherwise: use Azure OpenAI with DefaultAzureCredential ... The foundry-local-sdk (v1.1.0+) handles everything — initializing the runtime, downloading models, and loading them: from foundry_local_sdk import FoundryLocalManager, Configuration # Initialize once (singleton) config = Configuration(app_name="my-app") manager = FoundryLocalManager(config) # Start OpenAI-compatible web service manager.start_web_service() print(manager.urls[0]) # SDK auto-discovers the endpoint # Download & load a model model = manager.catalog.get_model("qwen2.5-7b") model.download() model.load() # Chat directly — no web service needed chat = model.get_chat_client() response = chat.complete_chat([{"role": "user", "content": "Hello!"}]) Jupyter Notebook The repo includes a Jupyter notebook (foundry_local.ipynb) that walks you through: Installing Foundry Local Loading a model Sending chat completions (streaming and non-streaming) Running the full Engineering Squad in local-only mode Traceability — Every Run Is Versioned Every squad execution gets a unique Run ID and produces a structured artifact set: output/ runs/ 20260524_a3f9b1/ run_metadata.json ← run ID, timestamp, requirement hash, decision impact_classification.md user_stories.md technical_design.md code_changes.md ← change log (code is in real files) unit_test_results.md tests.md test_results.md review_feedback.md latest/ ← symlink to most recent approved run The run_metadata.json is structured for future Azure DevOps integration — auto-creating work items, tasks, and test cases from squad output. Two Ways to Run Mode Best For GitHub Copilot Agent Mode Existing codebases — Copilot has full workspace context via #codebase Python CLI (python main.py) New projects, CI pipelines, fully automated runs Running with GitHub Copilot Agent Mode This is the recommended way to run the squad on existing projects. Copilot has full access to your workspace — it can read files, write code, and run terminal commands — so it naturally understands your architecture, patterns, and conventions. Prerequisites VS Code with the GitHub Copilot and GitHub Copilot Chat extensions installed A Copilot subscription that supports Agent Mode (Copilot Pro, Business, or Enterprise) Setup Clone the repo and open it in VS Code: git clone https://github.com/prasunagga/engineeringSquad.git code engineeringSquad Switch to Agent Mode — In the Copilot Chat panel, click the mode dropdown (top of the chat input) and select "Agent". This is required — Ask and Edit modes don't have tool access. Enable tools — Click the 🔧 tools icon (or gear/settings icon) at the bottom of the chat input area. Make sure the following tools are enabled: File operations (read, create, edit files) Terminal (run commands) Code search / workspace context Without these enabled, the squad can't read your codebase or write code into files. Edit your requirement — Open requirements_input.txt and write your requirement: PROJECT_TYPE: existing FRONTEND_PATH: plant-catalog BACKEND_PATH: Build a cart page where users can add plants, adjust quantities, and see totals. Running the Squad In Copilot Chat (Agent Mode), type: /run-squad This triggers the .github/prompts/run-squad.prompt.md file — a prompt file with mode: agent in its YAML frontmatter that orchestrates the full workflow: --- mode: agent description: Run the full Engineering Squad workflow tools: - read_file - create_file - replace_in_file - insert_text - delete_file_range --- Copilot will then execute the full pipeline: read requirements → classify impact → generate stories → design → write code → write tests → run tests → code review → approve or loop back. How It Differs from Python CLI Copilot Agent Mode Python CLI Context Full workspace awareness via #codebase Reads files from paths in requirements_input.txt Human-in-loop Spec Agent asks you directly in chat Spec Agent prints questions to stdout Code editing Uses VS Code's file editing tools Writes files via Python open() Test execution Runs npm test / playwright test in VS Code terminal Runs via subprocess Model Uses whichever model is selected in Copilot Uses Azure OpenAI / Foundry Local Individual Agent Prompts The .github/prompts/ directory also contains standalone prompt files for running individual agents: Prompt Purpose run-squad.prompt.md Full orchestrated pipeline developer.prompt.md Developer agent only code-reviewer.prompt.md Code review only story-agent.prompt.md Generate user stories only technical-design.prompt.md Technical design only test-writer.prompt.md Write E2E tests only Extending the Framework The squad is designed to be modular. Here are the most common extension points: Add a New Agent Every agent follows the same pattern — a function that takes SquadState, calls an LLM, and returns updated fields: # agents/my_agent.py from langchain_core.prompts import ChatPromptTemplate from graph.state import SquadState from models.azure_llm import get_azure_llm, DEPLOYMENT_DEVELOPER PROMPT = ChatPromptTemplate.from_messages([ ("system", "You are a security review specialist."), ("human", "Review this code for vulnerabilities:\n{code}"), ]) def my_agent_node(state: SquadState) -> dict: llm = get_azure_llm(deployment=DEPLOYMENT_DEVELOPER) result = (PROMPT | llm).invoke({"code": state["code"]}) return {"security_review": result.content} Then wire it in: Add state fields in graph/state.py Register the node and edges in graph/workflow.py Add artifact output in main.py Swap the LLM for Any Agent Each agent calls get_azure_llm(deployment=...) or get_local_llm(). You can: Change the model — edit .env (e.g., AZURE_DEPLOYMENT_DEVELOPER=gpt-5.4) Go fully local — python main.py --local-only Use a different provider — replace get_azure_llm() with any LangChain-compatible LLM (Anthropic, Ollama, Groq, etc.) Customize Agent Prompts Each agent's system prompt is defined as a ChatPromptTemplate at the top of its file in agents/. Edit the prompt directly — no configuration layer to navigate. Change the Review Loop The routing logic lives in graph/workflow.py → route_review(). Add new decision strings, change the routing map, or adjust MAX_ITERATIONS (default: 5). VS Code Copilot Agent Mode The .github/prompts/ directory contains prompt files for running individual agents in VS Code Copilot Agent Mode. Edit these to customize agent behavior when running through Copilot. What I Learned Building This Structured output is essential for routing. Without Pydantic models for review decisions, the conditional edge routing would be fragile and string-matching-dependent. Impact classification saves significant time. Running 9 agents for a one-line config change is wasteful. Classifying scope first makes the system practical. The self-correcting loop works — but needs a hard stop. Left unchecked, agents can ping-pong feedback indefinitely. The 5-iteration cap is a pragmatic safety net. Hybrid local + cloud models are the right balance. Not every task needs GPT-4.1. User story generation and test writing work well on smaller local models, cutting costs without sacrificing quality. "Ask, don't guess" is the single most important principle. When the Spec Agent encounters ambiguous requirements, it stops and asks the user rather than hallucinating assumptions. This one rule prevents the most costly category of errors. Try It Yourself The framework is open source and designed to be extensible: git clone https://github.com/prasunagga/engineeringSquad.git cd engineeringSquad pip install -r requirements.txt # Edit your requirement notepad requirements_input.txt # Run (local-only, no Azure needed) python main.py --local-only Requirements: Python 3.10+ Windows, macOS, or Linux For local-only: Foundry Local (winget install Microsoft.FoundryLocal) For cloud mode: Azure OpenAI endpoint + az login What's Next Azure DevOps MCP integration — Auto-sync stories, tasks, and test cases to ADO boards CI/CD trigger — Auto-run the squad on PR creation or work item assignment Multi-repo support — Frontend, backend, and infra in separate repositories Cost estimation — Estimate effort and cloud costs from the technical design Links GitHub: github.com/prasunagga/engineeringSquad Foundry Local docs: learn.microsoft.com/en-us/azure/foundry-local/what-is-foundry-local LangGraph docs: langchain.com/langgraph Azure OpenAI docs: azure.microsoft.com/en-us/products/ai-foundry/models/openai
Building Agentic Systems on Azure: Microsoft Foundry Agents SDK vs Microsoft Agent Framework
In my recent experience as a Senior Consultant at Microsoft, I’ve been actively involved in designing and delivering AI-driven solutions, with a strong focus on building intelligent agents using modern frameworks. Along the way, I've built agents using both Microsoft Foundry Agents SDK (hereafter "Agents SDK") and Microsoft Agent Framework (MAF) Both approaches are powerful and capable. However, once you move beyond simple proofs of concept, the developer experience and architectural patterns start to differ significantly. This article provides a practical comparison based on real implementation experience and aims to help developers choose the right approach. Approach 1: Agents SDK Agents SDK provides a straightforward way to create agents with integrated tools and models. Example: Creating an Agent from azure.ai.projects import AIProjectClient from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType from azure.identity import DefaultAzureCredential client = AIProjectClient(credential=DefaultAzureCredential(), endpoint=os.getenv("AZURE_AI_PROJECT_ENDPOINT")) # Configure tools ai_search = AzureAISearchTool( index_connection_id=conn_id, index_name="my-index", query_type=AzureAISearchQueryType.SEMANTIC, ) # Create agent (persisted in Foundry portal) agent = client.agents.create_agent( model=os.getenv("AZURE_AI_AGENT_DEPLOYMENT_NAME"), name="MyAgent", instructions="You are a helpful assistant.", tool_resources=ai_search.resources, tools=ai_search.definitions, ) # Run conversation thread = client.agents.threads.create() client.agents.messages.create(thread_id=thread.id, role="user", content="Hello") run = client.agents.runs.create(thread_id=thread.id, agent_id=agent.id) What this approach provides Native integration with Azure AI services (OpenAI, AI Search, MCP) Managed execution environment Simple and quick agent setup Conceptually, this approach can be summarized as: Model + Tools + Execution Strengths ✅ Rapid development and onboarding ✅ Strong integration within the Azure ecosystem ✅ Well-suited for single-agent or tool-driven use cases ✅ Minimal infrastructure overhead Challenges observed in practice As the complexity of scenarios increases, certain limitations become more visible: Multi-agent workflows require custom orchestration logic Agent handoffs must be implemented manually Context sharing across agents requires additional design effort While this approach offers flexibility, it shifts orchestration complexity to the developer. Approach 2: Microsoft Agent Framework (MAF) Microsoft Agent Framework introduces a higher-level abstraction, focused on agent orchestration and system design. Creating an Agent from agent_framework import Agent, WorkflowBuilder, Message from agent_framework.foundry import FoundryChatClient from azure.identity import DefaultAzureCredential client = FoundryChatClient( project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"), model=os.getenv("FOUNDRY_MODEL_DEPLOYMENT_NAME"), credential=DefaultAzureCredential(), ) # Create agents (in-process only, not persisted in portal) researcher = Agent(client, name="ResearcherAgent", instructions="Research topics thoroughly.") writer = Agent(client, name="WriterAgent", instructions="Write concise summaries.") # Build and run multi-agent workflow workflow = WorkflowBuilder(start_executor=researcher).add_edge(researcher, writer).build() async for event in workflow.run(Message("user", "Summarize migration best practices"), stream=True): print(event.content) What this approach provides Built-in orchestration capabilities Native support for multi-agent workflows Structured agent lifecycle management Context and memory handling Conceptually, this can be viewed as: Agents + Orchestration + System Design Observations from implementation When implementing similar use cases using MAF: Agent responsibilities became clearly defined Routing and delegation patterns were significantly simplified Overall system architecture became easier to maintain and scale This approach encourages thinking in terms of agent ecosystems rather than isolated agents. Architecture Comparison Agents SDK Microsoft Agent Framework (MAF) Choosing the Right Approach Use Agents SDK when: You need rapid development for a single-agent use case The workflow is relatively straightforward You prefer flexibility and lower-level control Use Microsoft Agent Framework when: You are designing multi-agent systems Your solution requires routing, delegation, or handoffs Long-term scalability and maintainability are essential Pros and Cons Summary Agents SDK Pros Easy to get started Strong Azure integration Flexible design Cons Manual orchestration required Limited native multi-agent support Complexity increases as scenarios grow Microsoft Agent Framework (MAF) Pros Built-in orchestration Native multi-agent support Scalable and structured architecture Cons Learning curve for new developers More opinionated framework design Reduced low-level control compared to SDK-based approach References and Repositories 🔗 Microsoft Agent Framework (MAF) Microsoft Agent Framework – GitHub Repository Microsoft Agent Framework Samples – Tutorials & Examples Workflow Samples (Multi-agent patterns) FoundryChatClient sample (Python) Agent Framework demos - GitHub Source 📘 Documentation Microsoft Agent Framework Overview (Microsoft Learn) Agent Framework + Microsoft Foundry provider docs 🔗 Azure AI Projects / Agents SDK Azure AI Projects SDK – Python (GitHub Source) Azure AI Projects Agents (.NET SDK repo) 📘 Documentation Azure AI Projects SDK (Python) – Microsoft Learn Azure AI Agents SDK – Microsoft Learn Conclusion Azure AI Projects and Microsoft Agent Framework both play important roles in the modern agent development landscape. Agents SDK enables quick and flexible agent development Microsoft Agent Framework enables structured, scalable agent systems In practice, the choice depends on whether you are building a single agent feature or a multi-agent system. Final Thought Agents SDK helps you get started quickly. Microsoft Agent Framework helps you scale with confidence In a follow-up blog, I’ll dive into how the M365 Agents SDK compares with Microsoft Agent Framework, especially in the context of enterprise productivity and Copilot experiences.Building an On-Device Voice Assistant with Microsoft Foundry Local
Why on-device voice still matters Most "voice AI" tutorials assume your audio leaves the machine. You ship a WAV to Whisper-API, your transcript to GPT-4, and a synthesized response back over the wire. That works — but it also means three round trips, three per-token bills, and three places your user's voice gets logged. The new wave of small, hardware-optimised models changes the trade-off. NVIDIA's Nemotron Speech Streaming En 0.6B is a 600M-parameter streaming ASR model published into the Microsoft Foundry Local catalog. Paired with a small chat model like qwen2.5-0.5b or phi-4-mini , you can run the entire capture → transcribe → reason → respond loop in-process on a developer laptop, with no API keys and no network egress. This post walks through how the fl-nemotron sample does it, the SDK pitfalls we hit on the way, and the design decisions that made the pipeline reliable. What we're building A browser-hosted assistant served by FastAPI at http://127.0.0.1:8000 . The page captures microphone audio, posts it to /api/transcribe , then streams the chat reply back over Server-Sent Events from /api/chat . All inference runs locally through two Foundry Local models loaded into the same process. The shape of the pipeline: Microphone (browser MediaRecorder) │ WebM/Opus blob ▼ Client-side WAV encoder (16 kHz, mono, PCM-16) │ multipart/form-data ▼ FastAPI /api/transcribe │ ▼ Nemotron Speech Streaming En 0.6B (Foundry Local audio client) │ transcript text ▼ Chat LLM e.g. qwen2.5-0.5b (Foundry Local chat client) │ streamed tokens ▼ FastAPI /api/chat → SSE → browser bubble The version that bit us: foundry-local-sdk >= 1.1.0 Before any code, the single most important fact about this project: The Nemotron Speech Streaming model only appears in the Foundry Local 1.1.x catalog. Older SDKs (0.5.x / 0.6.x) cannot resolve the alias nemotron-speech-streaming-en-0.6b and fail with model not found . The module name also changed in 1.1.0 — it is now foundry_local_sdk (with the underscore- sdk suffix), not foundry_local . The pip wheel for foundry-local-core is bundled, so there is no separate MSI / winget install to worry about. Pin it explicitly: pip install --upgrade "foundry-local-sdk>=1.1.0,<2" And verify before anything else: python -c "import importlib.metadata as m; print('sdk', m.version('foundry-local-sdk'))" # expect: sdk 1.1.0 Loading both models from one manager The 1.1.x SDK exposes a single FoundryLocalManager that owns the runtime. Each loaded model gives you back a per-model OpenAI-compatible client — get_chat_client() for text models and get_audio_client() for ASR. There is no need to bring your own openai Python package; the SDK ships its own thin client. The wrapper used in the repo ( src/foundry_client.py ) does this: from foundry_local_sdk import Configuration, FoundryLocalManager FoundryLocalManager.initialize(Configuration(app_name="fl-nemotron")) manager = FoundryLocalManager.instance chat_model = manager.load_model("qwen2.5-0.5b") stt_model = manager.load_model("nemotron-speech-streaming-en-0.6b") chat_client = chat_model.get_chat_client() audio_client = stt_model.get_audio_client() Both models are downloaded on first use into the Foundry Local cache and stay resident for the lifetime of the process. On a laptop with 16 GB RAM, the combined working set sits comfortably under 4 GB. The transcription surprise The first naive approach was the obvious one: with open(wav_path, "rb") as f: result = audio_client.transcribe(file=f, model="nemotron-speech-streaming-en-0.6b") That call fails on Nemotron. The bundled ONNX Runtime GenAI in foundry-local-core does not register the nemotron_speech multi-modal model type that the standard AudioClient.transcribe() path tries to instantiate. The error surfaces as a cryptic model-type registration failure deep inside the native runtime. The fix is to use the streaming session API instead — a different native entry point ( core_interop.start_audio_stream ) that the streaming model does support. The repo isolates this in src/_nemotron_live.py : def transcribe_wav_live(audio_client, wav_path, *, language="en"): with wave.open(str(wav_path), "rb") as w: sample_rate = w.getframerate() channels = w.getnchannels() sample_width = w.getsampwidth() pcm = w.readframes(w.getnframes()) session = audio_client.create_live_transcription_session() session.settings.sample_rate = sample_rate session.settings.channels = channels session.settings.bits_per_sample = sample_width * 8 session.settings.language = language session.start() # Feed PCM in ~100 ms chunks from a worker thread, then stop. bytes_per_sec = sample_rate * channels * sample_width chunk_bytes = max(bytes_per_sec // 10, 1024) def _pusher(): try: for offset in range(0, len(pcm), chunk_bytes): session.append(pcm[offset:offset + chunk_bytes]) finally: session.stop() threading.Thread(target=_pusher, daemon=True).start() parts = [] for resp in session.get_stream(): for cp in getattr(resp, "content", []) or []: text = getattr(cp, "text", "") or getattr(cp, "transcript", "") or "" if text: parts.append(text) return " ".join(p.strip() for p in parts if p.strip()).strip() Two things to notice: Push from a thread, read from the main coroutine. session.append() is a blocking write into the native stream and session.get_stream() is a blocking generator. Run one in a worker thread so the other can drain in parallel — otherwise you deadlock the session. Chunk to ~100 ms. Smaller chunks (e.g. 10 ms) spend more time crossing the FFI boundary than transcribing; larger chunks (e.g. 1 s) hold back partial results and hurt perceived latency. Always session.stop() . Without it the generator never terminates and the request hangs. The other transcription surprise: browsers don't send WAV Inside the browser, MediaRecorder defaults to audio/webm; codecs=opus . That's great for size but bad for our STT model, which expects a 16-bit mono PCM WAV at a known sample rate. Decoding WebM/Opus server-side would require ffmpeg as a runtime dependency — which is exactly the kind of friction this project exists to remove. The cleaner solution is to encode WAV on the client. AudioContext.decodeAudioData already understands WebM/Opus, so the page can decode the recording, resample to 16 kHz, mix to mono, and emit a PCM-16 WAV blob in 30 lines of JavaScript: // Inside src/static/index.html async function webmToWav(blob) { const ctx = new (window.AudioContext || window.webkitAudioContext)({ sampleRate: 16000 }); const buf = await ctx.decodeAudioData(await blob.arrayBuffer()); // Mix to mono const ch = buf.numberOfChannels; const mono = new Float32Array(buf.length); for (let c = 0; c < ch; c++) { const data = buf.getChannelData(c); for (let i = 0; i < data.length; i++) mono[i] += data[i] / ch; } return encodeWav(mono, 16000); } function encodeWav(samples, sampleRate) { const buffer = new ArrayBuffer(44 + samples.length * 2); const view = new DataView(buffer); // RIFF header writeStr(view, 0, "RIFF"); view.setUint32(4, 36 + samples.length * 2, true); writeStr(view, 8, "WAVE"); // fmt chunk writeStr(view, 12, "fmt "); view.setUint32(16, 16, true); // PCM chunk size view.setUint16(20, 1, true); // PCM format view.setUint16(22, 1, true); // mono view.setUint32(24, sampleRate, true); view.setUint32(28, sampleRate * 2, true); // byte rate view.setUint16(32, 2, true); // block align view.setUint16(34, 16, true); // bits per sample // data chunk writeStr(view, 36, "data"); view.setUint32(40, samples.length * 2, true); // PCM-16 samples let o = 44; for (let i = 0; i < samples.length; i++, o += 2) { const s = Math.max(-1, Math.min(1, samples[i])); view.setInt16(o, s < 0 ? s * 0x8000 : s * 0x7FFF, true); } return new Blob([view], { type: "audio/wav" }); } Now the server's /api/transcribe endpoint just writes the bytes to a temp file and hands them to transcribe_wav_live() — no audio decoding libraries on the Python side. Wiring it into FastAPI The server ( src/app.py ) is deliberately small. The notable detail is that the same process holds both Foundry Local model handles for its entire lifetime, so there is no warm-up cost per request: @app.post("/api/transcribe") async def transcribe(audio: UploadFile = File(...)): data = await audio.read() with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: f.write(data); path = f.name text = _ai_client.transcribe(path) return {"text": text} @app.post("/api/chat") async def chat(req: ChatRequest): if req.stream: return StreamingResponse( _sse(_ai_client.stream_completion(req.messages)), media_type="text/event-stream", ) return {"text": _ai_client.chat_completion(req.messages)} Streaming uses Server-Sent Events because they are trivially supported in both fetch() and the FastAPI runtime, and they don't require a WebSocket upgrade through any proxy a developer might have in front of localhost . What it looks like The repo includes screenshots of the running UI: a welcome screen with both models loaded, a streamed haiku reply, an inline code block with copy-to-clipboard, and the recording state for the microphone. Performance, honestly This is a small-model, CPU-friendly stack. On an Arm64 Surface running the x64 SDK under emulation: First model load (cold cache): tens of seconds — downloads ~600 MB for Nemotron and ~400 MB for qwen2.5-0.5b . Subsequent loads (warm cache): a few seconds per model. End-to-end transcription of a 5-second utterance: well under a second after warm-up. First chat token from qwen2.5-0.5b : typically 200–500 ms; full short reply within 1–2 s. On x64 silicon with a recent CPU the numbers improve substantially, and the SDK will pick the best execution provider it finds (CPU / DirectML / CUDA) for each model. Trade-offs to know about Model quality. qwen2.5-0.5b is a 500M-parameter model. It is fast and small enough to ship on a laptop, but it is not GPT-4. Swap in phi-4-mini or mistral-nemo-12b-instruct if you have the RAM and want better reasoning — the wrapper accepts any chat alias in the Foundry Local catalog. STT is English-only here. The current Nemotron streaming model in the catalog is ...-en-0.6b . Multilingual variants are likely to follow. Browser microphone needs a real browser. Headless / automated browsers (Playwright, Puppeteer) deny getUserMedia by default. Open the page in Edge / Chrome / Firefox to grant the permission and capture audio for real. No agent framework yet. This sample is deliberately a single-turn loop over a chat client — there is no tool calling, planning, or multi-agent orchestration. Adding the Microsoft Agent Framework on top would be a natural next step for richer behaviour. Responsible AI considerations Running locally removes the cloud-egress class of privacy concerns, but it does not remove responsibility: Disclose recording. The browser prompts for mic permission; your UI should make it obvious when capture is active. The sample shows a red ⏹ button and a "Recording…" banner for that reason. Don't log raw audio. The sample writes audio to a per-request NamedTemporaryFile and deletes it after transcription. Treat the WAV as sensitive data even when it never leaves the device. Small models hallucinate. A 0.5B chat model is great for snappy local replies, but unsuitable for high-stakes answers. Pair it with retrieval, ground it on your own data, or escalate to a larger model when accuracy matters. Try it Clone github.com/leestott/fl-nemotron. ./setup.ps1 (or ./setup.sh ) to create a virtualenv and install the pinned SDK. python scripts/prefetch.py nemotron-speech-streaming-en-0.6b qwen2.5-0.5b to download both models. .venv\Scripts\uvicorn.exe app:app --app-dir src --port 8000 Open http://127.0.0.1:8000 in a real browser and click the 🎤 button. Where to go next Foundry Local documentation — official docs for the runtime, catalog, and SDK. microsoft/Foundry-Local — upstream samples and issue tracker. NVIDIA Nemotron model family — background on the speech and language models being published into the catalog. leestott/fl-nemotron — the full source for this post. Key takeaways Pin foundry-local-sdk >= 1.1.0 . Earlier SDKs cannot see the Nemotron Speech Streaming model. Use the LiveAudioTranscriptionSession API for Nemotron, not AudioClient.transcribe() . Encode WAV in the browser. It eliminates a heavy server-side ffmpeg dependency for a few lines of JS. Push audio chunks on a worker thread and drain the response generator on the main one to avoid deadlocks. A small Foundry Local chat model plus Nemotron STT gives you a credible local voice loop in a single Python process — no cloud, no keys, no data egress.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 Link
Building an End-to-End Azure RAG Strategy Agent with MS Foundry
High-Level Architecture This architecture represents an end-to-end Retrieval-Augmented Generation (RAG) pipeline where raw documents are ingested from Azure Blob Storage, processed using Document Intelligence, transformed into embeddings via Azure OpenAI, and indexed in Azure AI Search for hybrid retrieval. A Foundry/MAF-based agent orchestrates query processing by combining user input with relevant search results and generates contextual responses, which are exposed through a FastAPI or CLI interface. This solution is composed of two main layers: 1. Data Ingestion Layer (RAG Pipeline) This layer transforms raw enterprise documents into searchable knowledge. Flow: Raw documents stored in Azure Blob Storage Supported formats: PDF, DOCX, PPTX, images, etc. Document Intelligence extraction Extracts: Text Tables Key-value pairs Structure Writes output as structured JSON back to Blob (processed/) Chunking + Embedding Documents are split into chunks Each chunk is embedded using Azure OpenAI (text-embedding-*) Indexing into Azure AI Search Creates a hybrid index: Keyword search Semantic ranking Vector search Enables flexible retrieval strategies 2. Query Layer (Strategy Agents) This layer enables intelligent query answering. Flow: User sends a query via: FastAPI endpoint CLI interface Query is handled by: Microsoft Agent Framework (MAF) agent Running on Azure AI Foundry Agent: Queries Azure AI Search Retrieves top relevant chunks Injects them into LLM prompt LLM generates grounded response This follows the standard RAG pattern: Retrieval → Augmentation → Generation End-to-End Flow Key Azure Services Used Service Purpose Azure Blob Storage Raw + processed document storage Azure AI Document Intelligence Extract structured content Azure OpenAI Embeddings + LLM generation Azure AI Search Hybrid retrieval engine Azure AI Foundry Agent orchestration Microsoft Agent Framework Agent execution layer Why this Architecture Matters This solution goes beyond basic RAG and provides: Hybrid Retrieval Combines keyword + semantic + vector search Improves recall and accuracy Structured Document Parsing Handles complex enterprise documents Extracts tables and metadata Agent-Based Orchestration Enables reasoning over retrieval results Extensible for multi-agent workflows Scalable Data Pipeline Supports continuous ingestion Works with large document collections Enterprise Considerations Use Managed Identity for secure service access Apply RBAC on Cosmos DB / Search / Storage Enable Private Endpoints for network isolation Use Guardrails + Evaluations in Foundry Summary This repository demonstrates a production-ready Azure RAG architecture: Ingest → Extract → Chunk → Embed → Index Retrieve → Reason → Generate Powered by Azure AI Foundry + Agent Framework By combining data engineering + AI orchestration, it enables enterprise AI systems that are: Accurate Grounded Extensible Repo: https://github.com/snd94/azure-rag-strategy-agent Please refer to the Microsoft Learn Documentation for further information: Azure AI Search documentation - Azure AI Search | Microsoft Learn Document Intelligence documentation - Quickstarts, Tutorials, API Reference - Foundry Tools | Microsoft Learn How to generate embeddings with Azure OpenAI in Microsoft Foundry Models - Microsoft Foundry | Microsoft Learn How to generate embeddings with Azure OpenAI in Microsoft Foundry Models - Microsoft Foundry | Microsoft Learn Microsoft Agent Framework Overview | Microsoft Learn What is Microsoft Foundry? - Microsoft Foundry | Microsoft LearnThe Cloud Foundation for Safe Agentic AI
Why enterprise agents need more than a working prototype Most AI conversations start with the model. Which model should we use? Which framework? Which agent platform? Which demo can we build quickly enough to make the idea feel real? Those questions are not wrong, but they are rarely the first questions that matter in an enterprise environment. In real projects, the hard part usually appears after the first prototype works. The demo can answer a question, call a tool, retrieve a document, or update a record. Then someone asks whether it can be connected to production data, used by more teams, or allowed to trigger real actions. That is where the conversation changes. In the first part of this series, I looked at why many companies are less ready for agentic AI than they think. The blockers were practical and familiar: unclear business problems, immature processes, weak data foundations, and no clear owner when an AI system makes a poor recommendation or takes a wrong action. The message was simple: Before a company asks what agents can do, it needs to understand what it is ready to delegate. But business readiness is only the first layer. Even when the use case is clear, the process is understood, and leadership is aligned, another question appears. Is the platform ready to support agents safely? This is where Part 2 begins. Agentic AI does not behave like a normal application workload. A traditional application usually follows predefined paths. It receives a request, processes logic, returns a response, writes to a database, or calls an API. Agents introduce a different pattern. They reason over context, retrieve information, choose tools, trigger actions, interact with other services, and sometimes operate across multiple systems at once. That makes the surrounding cloud platform much more important. There is also a shadow AI angle to this. In many organizations, agent-like capabilities are already entering through SaaS platforms, vendor copilots, browser extensions, and productivity tools. These systems may not run inside the organization’s governed Azure subscriptions, but they can still interact with enterprise data and business workflows. If the official platform is not ready, teams will often find less governed ways to experiment anyway. That is not always malicious. Sometimes it is just people trying to solve their work with the tools available to them. The marketing analyst pasting customer data into a public chatbot because the official AI platform is six months away. The support team using a browser extension that summarizes tickets, without anyone realizing those tickets are also being sent to a third-party service. From a governance point of view, the effect is the same. Cloud readiness for agentic AI is not defined by access to cloud services or model endpoints alone. The real question is whether the platform can support controlled autonomy. Before enterprises can trust agents to act, the platform must be able to identify them, observe their behavior, restrict their permissions, enforce policy, and contain failure. Without that, an organization is not really deploying an intelligent assistant in a controlled way. It is introducing a workload that can interact with enterprise systems without anyone clearly watching what it does or being able to stop it. From business readiness to cloud readiness After the business foundation is clear, the next layer is the cloud foundation. A company may have a strong use case, executive support, and even a working prototype. But that does not mean it is ready to deploy agents in production. A prototype can run with broad access, manual supervision, loose logging, and a small group of test users. Production requires more discipline. It requires clear identity, controlled access, traceable activity, enforceable policy, and operational ownership. Cloud readiness for agentic AI comes down to four pillars, in this order: Identity-first architecture Observability Policy controls Platform constraints The order matters. 1. Identity-first architecture Identity comes first because nothing can be governed properly if it cannot be identified. In traditional cloud systems, we already learned this lesson with users, applications, service principals, managed identities, and workloads. Agents add another layer of non-human actors into the enterprise environment. If an agent can retrieve data, call tools, trigger workflows, or interact with business systems, it needs a clear identity. Without that foundation, governance becomes fragile. Teams may struggle to control what the agent can access, understand what it did, or determine who is accountable when something goes wrong. I have seen agents running in production where nobody could clearly say who owned them. They worked. Until they did not. Identity-first architecture means each agent or agentic workload should have a defined identity, ownership model, permission scope, and lifecycle. It should be clear whether the agent is acting on behalf of a user, acting as a service, or operating within a delegated boundary. This matters because permissions are not an implementation detail. They define the blast radius and accountability model of the system. In Azure environments, this is where Microsoft Entra ID and newer agent identity capabilities become important. As agents become more common across Azure AI Foundry, Copilot Studio, Microsoft 365, and custom frameworks, organizations need a way to understand which agents exist, who owns them, what they can access, and how their lifecycle is managed. Identity is not only about authentication. It is also about visibility, traceability, ownership, permission boundaries, and accountability. Agents should not remain hidden inside application logic or operate through shared identities. If they can retrieve data, call tools, or trigger actions, they need to be managed with the same care as any other production workload. 2. Observability Once identity is established, observability becomes the next pillar. Knowing that an agent exists is not enough. The platform must be able to show what the agent did. For normal applications, observability often focuses on service health, latency, failures, and resource usage. For agents, those signals still matter, but they are incomplete. Agent observability also needs to capture the execution path across model calls, retrieved context, orchestration steps, tool calls, policy decisions, approvals, denials, and final actions. This changes how we think about monitoring. With agentic systems, the question is not only whether a request succeeded or failed. Teams also need to understand the path that led to the outcome, the context used, the tools called, the policies applied, and the point where behavior changed. Without that visibility, it is difficult to investigate failures and improve reliability. This is also where observability starts to support governance, not just troubleshooting. Once teams can measure how agents behave, they can move toward KPI-based governance. That may include reliability, escalation rates, policy denials, grounding quality, tool-call failures, cost per interaction, latency, and business outcome metrics. Without this measurement layer, maturity remains mostly opinion-based. With it, governance becomes evidence-based. In Azure, Azure Monitor is the obvious starting point. Together with services such as Application Insights and Log Analytics, it provides the telemetry foundation needed to understand how AI workloads behave in production. For agentic systems, this usually requires combining platform telemetry with application-level traces from orchestration, retrieval, model calls, policy decisions, and tool execution. This visibility is what makes continuous improvement possible. It is also what allows governance to mature from “we think the agent is behaving correctly” to “we can measure how the agent behaves over time.” Small difference. Large consequence. 3. Policy controls The third pillar is policy controls. This comes after identity and observability because policy needs both. Identity defines who or what the rule applies to. Observability helps teams understand whether the rule is effective, bypassed, misconfigured, or too restrictive. Policy controls define the boundaries for what agents are allowed to do. They determine how agents access data, which tools they can use, which environments are in scope, when approval is required, and when an action or response should be blocked. The key point is simple: Prompts can guide behavior, but they are not a reliable enforcement layer. For enterprise systems, policy needs to be external, testable, auditable, and enforceable. This becomes especially important because agents may operate across multiple systems. An agent may retrieve information from one source, reason over the result, call a tool, update a ticket, send a message, or trigger a workflow. Each step may appear safe in isolation, while the full chain creates risk. Policy controls provide boundaries around that chain. In Azure, this starts at the cloud governance layer. Azure landing zones, management group structures, and Azure Policy can help define where AI workloads are deployed, how environments are separated, and which rules apply consistently across subscriptions. At runtime, Azure AI Content Safety can help detect harmful content, prompt attacks, unsafe interactions, or outputs that drift away from the intended task. For tool and API access, Azure API Management can also be used as a controlled gateway between agents and downstream systems. This can support centralized authentication, throttling, mediation, logging, and policy enforcement. It is not mandatory in every design, but it is a useful option when agents need governed access to APIs instead of direct backend connectivity. The goal is not to create friction for the sake of control. The goal is to make sure the agent operates inside boundaries that are defined outside the prompt and outside the model response. 4. Platform constraints The fourth pillar is platform constraints. This area often receives less attention early in the project, but it strongly shapes whether an agentic system can operate safely and reliably in production. These constraints include network isolation, private connectivity, data residency, regional availability, quota limits, model throughput, latency, logging retention, integration boundaries, cost behavior, and operational ownership. They may seem like implementation details during early design discussions, but they often determine whether the system can actually run in production. For agentic workloads, these constraints also shape where experimentation happens. Sandboxed environments, isolated subscriptions, limited tool access, and controlled test data can help teams evaluate agent behavior before exposing it to production systems. This becomes even more important when agents are allowed to generate code, call external tools, or execute actions that may not be fully trusted at design time. Platform constraints are where the earlier pillars meet implementation reality. Identity affects how agents connect to services. Observability affects logging cost, retention, and investigation capability. Policy affects routing, network design, tool exposure, and user experience. By the time an agentic system reaches production, these constraints are no longer background details. They become design boundaries. In Azure, this is where landing zone design, private networking, regional planning, quota management, cost management, and operational runbooks matter. Azure landing zones, private endpoints, private DNS, Azure Firewall, NSGs, and controlled network paths all influence whether the agent architecture can move from prototype to production without being redesigned halfway through. And yes, that redesign usually happens at the least convenient moment. Architecture has a sense of humor. Not a kind one. From principles to Azure capabilities The four pillars are not only architectural principles. They need to be translated into platform capabilities, operating practices, and governance controls. In practice, controlled agent deployment is rarely achieved by a single product or service. It requires multiple layers working together. Identity, monitoring, policy, networking, runtime safety, API exposure, and operational controls all play a part. Azure provides several services and patterns that can help implement these controls, but there is no fixed blueprint that applies to every organization. The right combination depends on the use case, regulatory requirements, existing landing zone design, integration landscape, and the level of autonomy expected from the agent. The examples below should be seen as a practical toolset, not as a mandatory checklist. Pillar Goal Example Azure capabilities Identity-first architecture Make agents visible, owned, permissioned, and governable as enterprise workloads. Microsoft Entra ID, Microsoft Entra Agent ID, managed identities, service principals, workload identities, access reviews, Conditional Access, Privileged Identity Management Observability Understand runtime behavior, trace execution paths, investigate failures, and improve reliability. Azure Monitor, Application Insights, Log Analytics, Azure AI Foundry tracing, diagnostic settings, distributed tracing, correlation IDs, application-level telemetry Policy controls Enforce boundaries around access, actions, content safety, APIs, and governance. Azure landing zones, management groups, Azure Policy, Azure AI Content Safety, Prompt Shields, Microsoft Purview, Azure API Management, RBAC, approval flows Platform constraints Operate within real cloud boundaries such as networking, region, quota, compliance, and operations. Azure landing zones, private endpoints, private DNS, private networking, Azure Firewall, NSGs, quota planning, regional architecture, cost management The purpose of this mapping is not to suggest that Azure has one single service for each pillar. It does not. The practical goal is to combine the right services and patterns so the platform can identify agents, monitor their behavior, enforce boundaries, and operate within known cloud constraints. Conclusion Agentic AI does not become enterprise-ready simply because a model is available, a prototype works, or a business sponsor is excited. The real question is whether the surrounding cloud foundation can support agents that act within boundaries the platform actually enforces. Together, these pillars move the discussion from building an agent to preparing the environment in which the agent can operate responsibly. That distinction is important. A prototype can rely on broad access, limited logging, and close manual supervision. A production system needs clearer boundaries around ownership, access, traceability, and control. This is also where the series moves naturally into Part 3. Once the business foundation is clear and the cloud foundation is in place, the next challenge is the design of the agent itself. The cloud foundation matters here because it provides the controlled environment in which agents can be tested, limited, and observed before they are trusted with broader enterprise access. For more advanced scenarios, that also includes sandboxing patterns for generated code, tool execution, and untrusted actions. In Part 3, I will move closer to implementation and look at how to design an enterprise-ready agent. That means defining the agent’s scope, grounding it with reliable knowledge, deciding which tools it can use, designing safe execution loops, adding human oversight where it matters, and thinking carefully about when a single agent is enough versus when multi-agent coordination is justified. That is where agentic AI starts becoming more than an idea. And, as usual, that is also where the architecture starts to matter. This article is part of my Agentic AI readiness series and was also published on Medium.Learn how to host your agents on Microsoft Foundry
We just concluded Host your agents on Foundry, a three-part livestream series where we explored how to deploy and host Python AI agents on Microsoft Foundry: Deploying Python agents to Foundry Hosted agents using the Azure Developer CLI Building hosted agents with Microsoft Agent Framework, including Foundry IQ integration and multi-agent workflows Building hosted agents with LangChain + LangGraph, including built-in tools like Bing Web Search Running quality and safety evaluations: bulk, scheduled, and continuous evals, guardrails, and red-teaming All of the materials from our series are available for you to keep learning from, and linked below: Video recordings of each stream PowerPoint slides that you can use for reviewing or even teaching the material to your own community Open-source code samples you can run yourself in your own Microsoft Foundry project Spanish speaker? Check out the Spanish version of the series. 🙋🏽♂️ Have follow up questions? Join the weekly Python+AI office hours on Foundry Discord. Host your agents on Foundry: Microsoft Agent Framework 📺 Watch YouTube recording In our first session, we deploy agents built with Microsoft Agent Framework (the successor of Autogen and Semantic Kernel). Starting with a simple agent, we add Foundry tools like Code Interpreter, ground the agent in enterprise data with Foundry IQ, and finally deploy multi-agent workflows. Along the way, we use the Foundry UI to interact with the hosted agent, testing it out in the playground and observing the traces from the reasoning and tool calls. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-agentframework-demos 📝 Write-up for this session Host your agents on Foundry: LangChain + LangGraph 📺 Watch YouTube recording In our second session, we deploy agents built with the popular open-source libraries LangChain and LangGraph. Starting with a simple agent, we add Foundry tools like Bing Web Search, ground the agent in Foundry IQ, then deploy more complex agents using the LangGraph orchestration framework. Along the way, we use the Foundry UI to interact with the hosted agent, testing it out in the playground and observing the traces from the reasoning and tool calls. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-langchain-demos 📝 Write-up for this session Host your agents on Foundry: Quality & safety evaluations 📺 Watch YouTube recording In our third session, we ensure that our AI agents are producing high-quality outputs and operating safely and responsibly. First we explore what it means for agent outputs to be high quality, using built-in evaluators to check overall task adherence and then building custom evaluators for domain-specific checks. With Foundry hosted agents, we run bulk evaluations on demand, set up scheduled evaluations, and even enable continuous evaluation on a subset of live agent traces. Next we discuss safety systems that can be layered on top of agents and audit agents for potential safety risks. To improve compliance with an organization's goals, we configure custom policies and guardrails that can be shared across agents. Finally, we ensure that adversarial inputs can't produce unsafe outputs by running automated red-teaming scans on agents, and even schedule those to run regularly as well. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-agentframework-demos 📝 Write-up for this sessionBuilding AI Agents with Microsoft Foundry: A Progressive Lab from Hello World to Self-Hosted
AI agent development has a steep on-ramp. The combination of new SDKs, tool-calling patterns, model selection decisions, retrieval-augmented generation, and deployment concerns means most developers spend more time wiring things together than actually building anything useful. The Microsoft Foundry Agent Lab is a structured, open-source demo series designed to change that — nine self-contained demos, each adding exactly one new concept, all built on the same Microsoft Foundry SDK and a single model deployment. This post walks through what the lab contains, how each demo works under the hood, and the architectural decisions that make it a useful reference for AI engineers building production agents. Why a Progressive Lab? Agent frameworks can be overwhelming. A developer who opens a rich example with RAG, tool-calling, streaming, and a custom UI all at once has no clear line of sight to which parts are essential and which are embellishments. The Foundry Agent Lab takes the opposite approach: start with the absolute minimum and introduce one new primitive per demo. By the time you reach Demo 8, you have seen every major capability — not in one monolithic sample, but in a layered sequence where each addition is visible and understandable. # Demo New Concept Tool Used UX 0 hello-demo Agent creation, Responses API, conversations None Terminal 1 tools-demo Function calling, tool-calling loop, live API FunctionTool Terminal 2 desktop-demo UI decoupling — same agent, different surface None Desktop (Tkinter) 3 websearch-demo Server-side built-in tools, no client loop WebSearchTool Terminal 4 code-demo Code execution in sandbox, Gradio web UI CodeInterpreterTool Web (Gradio) 5 rag-demo Document upload, vector stores, RAG grounding FileSearchTool Terminal 6 mcp-demo MCP servers, human-in-the-loop approval MCPTool Terminal 7 toolbox-demo Centralized tool governance, Toolbox versioning Toolbox Terminal 8 hosted-demo Self-hosted agent with Responses protocol Custom server Terminal + Agent Inspector The Model Router: One Deployment to Rule Them All Before diving into the demos, it is worth understanding the one architectural decision that ties the entire lab together: every agent uses model-router as its model deployment. MODEL_DEPLOYMENT=model-router Model Router is a Microsoft Foundry capability that inspects each request at inference time and routes it to the optimal available model — weighing task complexity, cost, and latency. A simple factual question goes to a fast, cheap model. A complex tool-calling chain with code generation gets routed to a frontier model. You write zero routing logic. The lab's MODEL-ROUTER.md file contains empirical observations from running all nine demos. A sample of what the router selected: Demo Query Task Type Model Selected hello "What's the capital of WA state?" Factual recall grok-4-1-fast-reasoning hello "Summarize our conversation" Summarization gpt-5.2-chat-2025-12-11 tools "What's the weather in Seattle?" Tool-using gpt-5.4-mini-2026-03-17 code Data analysis with code generation Code generation + execution gpt-5.4-2026-03-05 rag HR policy document question Retrieval + synthesis gpt-5.3-chat-2026-03-03 This is the strongest signal in the lab: you do not need to reason about model selection. You declare what your agent needs to do; the router handles the rest, and it chooses correctly. Demo 0: The Minimum Viable Agent The hello-demo establishes the baseline pattern used by every subsequent demo. Two files: one to register the agent, one to chat with it. Registering the agent from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition credential = DefaultAzureCredential() project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=credential) agent = project.agents.create_version( agent_name=AGENT_NAME, definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, instructions="You are a helpful, friendly assistant.", ), ) Authentication uses DefaultAzureCredential , which works with az login locally and with managed identity in production — no API keys anywhere in the code. Chatting with the agent # Create a server-side conversation (persists history across turns) conversation = openai.conversations.create() # Each turn sends the user message; the agent sees full history response = openai.responses.create( input=user_input, conversation=conversation.id, extra_body={"agent_reference": {"name": AGENT_NAME, "type": "agent_reference"}}, ) print(response.output_text) The conversation object is server-side. You pass its ID on every turn; the history lives in Foundry, not in a local list. This is the Responses API pattern — distinct from the older Completions or Chat Completions APIs. Demo 1: Function Tools and the Tool-Calling Loop Demo 1 adds function calling against a real weather API. The key insight here is that the model does not execute the function — it requests the execution, and your code executes it locally, then feeds the result back. Declaring a function tool from azure.ai.projects.models import FunctionTool, PromptAgentDefinition func_tool = FunctionTool( name="get_weather", description="Get the current weather for a given city.", parameters={ "type": "object", "properties": {"city": {"type": "string", "description": "City name"}}, "required": ["city"], }, strict=True, ) agent = project.agents.create_version( agent_name=AGENT_NAME, definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[func_tool], instructions="You are a weather assistant...", ), ) The tool-calling loop response = openai.responses.create(input=user_input, conversation=conversation.id, ...) # Loop while the model is requesting tool calls while any(item.type == "function_call" for item in response.output): input_list = [] for item in response.output: if item.type == "function_call": args = json.loads(item.arguments) result = get_weather(args["city"]) # execute locally input_list.append(FunctionCallOutput(call_id=item.call_id, output=result)) # Send results back to the agent response = openai.responses.create(input=input_list, conversation=conversation.id, ...) print(response.output_text) The strict=True parameter on FunctionTool enforces structured outputs — the model must return arguments that match the declared JSON schema exactly. This eliminates argument parsing errors in production. Demo 2: UI Is Not Your Agent Demo 2 runs the exact same agent as Demo 1 but surfaces it in a Tkinter desktop window. The point is pedagogical: your agent definition, conversation management, and tool-calling logic are entirely independent of your UI layer. Swapping from terminal to desktop requires changing only the presentation code — nothing in the agent or conversation path changes. This is a principle worth internalising early: agent logic and UI logic should never be entangled. The lab enforces this separation structurally. Demo 3: Server-Side Built-In Tools The web search demo introduces a sharp contrast with Demo 1. With WebSearchTool , the tool-calling loop disappears entirely from client code: from azure.ai.projects.models import WebSearchTool agent = project.agents.create_version( agent_name="Search-Agent", definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[WebSearchTool()], instructions="You are a research assistant...", ), ) The agent decides when to search, executes the search server-side, and returns a grounded response with citations. Your client code looks identical to Demo 0 — a simple responses.create() call with no tool loop. The distinction matters architecturally: Function tools (Demo 1) — tool execution happens on your client; you control the code, the API call, the error handling. Built-in tools (Demo 3+) — tool execution happens inside Foundry; you get results without managing execution. Demo 4: Code Interpreter and the Gradio Web UI Demo 4 attaches CodeInterpreterTool , which gives the agent a sandboxed Python execution environment inside Foundry. The agent can write code, run it, observe output, and iterate — all server-side. Combined with a Gradio web interface, this demo shows an agent that can perform data analysis, generate charts, and explain results through a browser UI. Model Router is particularly interesting here: the empirical data shows it selects a more capable frontier model ( gpt-5.4-2026-03-05 ) for code-generation tasks, while simpler conversational turns stay on lighter models. Demo 5: Retrieval-Augmented Generation with FileSearchTool Demo 5 introduces RAG. The setup phase uploads a document, creates a vector store, and attaches it to the agent: # Upload document and create a vector store vector_store = openai.vector_stores.create(name="employee-handbook-store") with open("data/employee-handbook.md", "rb") as f: openai.vector_stores.files.upload_and_poll( vector_store_id=vector_store.id, file=f ) # Attach the vector store to the agent agent = project.agents.create_version( agent_name="RAG-Agent", definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[FileSearchTool(vector_store_ids=[vector_store.id])], instructions="Answer questions using only the provided documents...", ), ) At query time, the agent embeds the question, searches the vector store semantically, retrieves matching chunks, and generates an answer grounded in the retrieved content — entirely server-side. The client code remains a plain responses.create() call. An important detail: the .vector_store_id file is written to disk during setup and read back during the chat session, so the demo survives process restarts without re-uploading the document. The .gitignore excludes this file from source control. Demo 6: Model Context Protocol Demo 6 connects the agent to a GitHub MCP server, giving it access to repository and issue data via the open Model Context Protocol standard. MCP servers expose tools over a standardised wire protocol; the agent discovers and calls them without any client-side function declarations. The demo also demonstrates human-in-the-loop approval: before executing any MCP tool call, the agent surfaces the proposed action and waits for the user to confirm. This is an important safety pattern for agents that can trigger side effects on external systems. Demo 7: Toolbox — Centralised Tool Governance Where Demo 6 connects to a single MCP server directly, Demo 7 uses a Toolbox — a managed Microsoft Foundry resource that bundles multiple tools into a single, versioned, MCP-compatible endpoint. The Toolbox in this demo exposes both GitHub Issues and GitHub Repos tools, curated into an immutable versioned snapshot. This pattern is significant for production multi-agent systems: Centralised governance — one team owns the tool definitions; all agents consume them via a single endpoint. Versioned snapshots — promoting a new Toolbox version is explicit; agents pin to a version and upgrade intentionally. MCP compatibility — any MCP-capable agent or framework can connect, not just Foundry SDK agents. from azure.ai.projects.models import McpTool toolbox_tool = McpTool( server_label="toolbox", server_url=TOOLBOX_ENDPOINT, allowed_tools=[], # empty = all tools in the Toolbox version headers={"Authorization": f"Bearer {token}"}, ) Demo 8: Self-Hosted Agent with the Responses Protocol The final demo departs from the prompt-agent pattern. Instead of registering a declarative agent in Foundry, Demo 8 implements a custom agent server using the Responses protocol. The server exposes a streaming HTTP endpoint; Foundry's Agent Inspector can connect to it and route user turns to it just as it would to a hosted prompt agent. This demo includes a Dockerfile and an agent.yaml , enabling deployment to Foundry's container hosting service. It uses gpt-4.1-mini directly rather than the model router, because the custom server owns the entire inference path. When to consider this pattern: Your agent requires custom pre- or post-processing logic that cannot be expressed in a system prompt. You need to integrate with infrastructure that is not reachable through MCP or built-in tools. You want to own the inference call for cost control, A/B testing, or compliance reasons. You are building a multi-agent orchestrator that needs to expose itself as an agent to other orchestrators. Getting Started The lab requires Python 3.10 or higher, an Azure subscription with a Microsoft Foundry project, and the Azure CLI. 1. Clone and set up the virtual environment git clone https://github.com/microsoft-foundry/Foundry-Agent-Lab.git cd Foundry-Agent-Lab # Create and activate the virtual environment python -m venv .venv # Windows Command Prompt .venv\Scripts\activate.bat # Windows PowerShell .venv\Scripts\Activate.ps1 # macOS / Linux source .venv/bin/activate pip install -r requirements.txt 2. Configure a demo copy hello-demo\.env.sample hello-demo\.env # Edit hello-demo\.env and set PROJECT_ENDPOINT Your PROJECT_ENDPOINT is on the Overview page of your Foundry project in the Azure portal. It takes the form https://your-resource.ai.azure.com/api/projects/your-project . 3. Run the demo az login 0-hello-demo Each numbered batch file at the root activates the virtual environment, runs create_agent.py , and launches chat.py . Append log to capture the full session transcript: 0-hello-demo log Reset between runs hello-demo\reset.bat Every demo includes a reset.bat that deletes the registered agent and any associated resources (vector stores, uploaded files). Demos are fully repeatable. Architecture Principles Demonstrated Across the nine demos, the lab illustrates a set of design principles that apply directly to production agent systems: Keyless authentication throughout Every demo uses DefaultAzureCredential . No API keys appear anywhere in the code. Locally, az login provides credentials. In production, managed identity takes over automatically — same code, no secrets to rotate. Server-side conversation state The Responses API stores conversation history server-side. Your application passes a conversation ID; Foundry maintains the thread. This eliminates the common bug of truncating history due to local list management and makes multi-process or multi-instance deployments straightforward. Client-side vs server-side tool execution The lab makes the distinction explicit. Function tools execute in your process — you control the code, the external call, and the error handling. Built-in tools (WebSearch, CodeInterpreter, FileSearch) execute inside Foundry — you get results without managing execution infrastructure. MCP tools (Demo 6, 7) fall between these: they execute in a separately deployed server, with the protocol mediating the call. Progressive tool introduction Each demo's create_agent.py registers the agent once. The chat.py file handles the conversation loop. These two responsibilities are always separate, making it easy to update agent definitions without modifying conversation logic, and vice versa. Security Considerations When building agents for production, keep the following in mind: Never commit .env files. The .gitignore excludes them, but verify this before pushing. Use Azure Key Vault or environment variable injection in CI/CD pipelines. Use managed identity in production. DefaultAzureCredential automatically picks up managed identity when deployed to Azure, eliminating the need for any stored credentials. Apply human-in-the-loop for side-effecting tools. Demo 6 demonstrates this pattern for MCP tool calls. Any agent that can modify external state (create issues, send emails, write files) should surface proposed actions for confirmation. Validate tool outputs before use. Treat data returned by external tools (weather APIs, search results, document retrieval) as untrusted input. Prompt injection through tool results is a real attack surface; grounding instructions in your system prompt reduce but do not eliminate this risk. Scope Toolbox permissions narrowly. When using a Toolbox (Demo 7), use allowed_tools to restrict which tools the agent can call, rather than granting access to all tools in a Toolbox version. Key Takeaways Start with the minimum. A prompt agent with no tools requires fewer than 30 lines of code using the Foundry SDK. Add tools only when the use case demands them. Use model-router unless you have a specific reason not to. The empirical data in the lab shows the router selects appropriate models across all task types — factual, creative, tool-calling, RAG, and code generation. Understand the client/server tool boundary. Function tools give you control; built-in tools give you simplicity. MCP and Toolbox give you governance and interoperability. Choose based on where you need control and where you need scale. Conversation state belongs on the server. Do not maintain conversation history in application memory if you can avoid it. The Responses API conversation object is designed for this. The hosted-demo pattern is for when you need to own the inference path. For most use cases, a declarative prompt agent is sufficient and far simpler to operate. Next Steps Explore the repo: github.com/microsoft-foundry/Foundry-Agent-Lab Microsoft Foundry SDK documentation: learn.microsoft.com/azure/ai-studio/ Responses API quickstart: Prompt agent quickstart Model Router conceptual documentation: Model Router for Microsoft Foundry Model Context Protocol: modelcontextprotocol.io Azure Identity SDK (DefaultAzureCredential): azure-identity Python SDK The Foundry Agent Lab is open source under the MIT licence. Contributions, bug reports, and feature requests are welcome through GitHub Issues. See CONTRIBUTING.md for guidelines.Agents League: The Esports-Inspired Hackathon Where AI Agents Battle for Glory
Ready to put your AI skills to the ultimate test? Agents League is here, a dynamic, esports-inspired developer challenge that brings the thrill of live competition to the world of agentic AI. Whether you're a seasoned AI developer or just getting started, this is your chance to build, compete, and win. What is Agents League? Agents League is a week-long hackathon running as part of AI Skills Fest (June 4–14, 2026). Unlike traditional hackathons, Agents League combines live AI coding battles, asynchronous project submissions, and a thriving Discord community all competing for a total prize pool of $55,000 USD. This isn't just about building it's about showcasing what's possible with agentic AI in a format that's fast, competitive, and globally accessible. Three Challenge Tracks Pick One or Compete in All 1. Creative Apps Build innovative applications using GitHub Copilot for AI-assisted development. Show off your creativity and demonstrate how AI can accelerate app creation from concept to code. 2. Reasoning Agents Create intelligent agents using Microsoft Foundry that solve complex problems through multi-step reasoning. This track is all about building agents that can think, plan, and execute. 3. Enterprise Agents Build business-ready knowledge agents integrated with Microsoft 365 Copilot, authored in Copilot Studio. Perfect for developers focused on real-world enterprise solutions. Live Microsoft Reactor Events—Don't Miss the Battles! The heart of Agents League beats through live Microsoft Reactor events. Watch experts go head-to-head in live coding battles, learn cutting-edge techniques, and get inspired for your own submissions: Event What You'll Learn Creative Apps Battle See GitHub Copilot in action as experts build innovative apps live Reasoning Agents Battle Watch multi-step reasoning agents come to life with Microsoft Foundry Enterprise Agents Battle Learn to build M365-integrated agents with Copilot Studio 👉 View the full event series Key Dates Registration Deadline: June 12, 2026, 12:00 PM PT Hacking Period: June 4–14, 2026 Submission Deadline: June 14, 2026, 11:59 PM PT What You Get Live coding battles with expert demonstrations Curated technical experiences and on-demand content Learning resources on Microsoft Learn and AI Skills Navigator Community support through Discord GitHub-based submissions for transparent, collaborative judging Why Participate? Agents League isn't just another hackathon. It's designed as a streamlined, competitive format that: ✅ Fits into your schedule with focused, time-boxed challenges ✅ Provides real-world product innovation experience ✅ Offers global accessibility—participate from anywhere ✅ Demonstrates the latest capabilities of agentic AI, including new IQ tools ✅ Connects you with a passionate developer community Ready to Enter the Arena? Register Now for Agents League Before you register: Review the Hackathon Rules and Regulations for prize categories and judging criteria Join the Microsoft Reactor event series for live battles and learning Check out the Microsoft Event Code of Conduct Join the Conversation Have questions? Want to connect with fellow competitors? Join the Agents League community on Discord and start strategizing with developers from around the world. Whether you're building creative apps, reasoning agents, or enterprise solutions—the arena awaits. May the best agent win! 🏆 Agents League hackathon is open to the public and offered at no cost. Government employees should check with their employers to ensure participation is permitted in accordance with applicable policies. Related Links: Agents League Hackathon Registration Microsoft Reactor Series AI Skills FestHow to Visualize Your Azure AI Workloads Usage for Observability
This article assumes you already have an Azure Foundry project and resource deployed in Microsoft Foundry. The options referenced here are documented in detail in the linked articles; this post serves as a consolidated step by step guide bringing them all together and explaining where each option is most useful. A Summary: Need Best Option Quick day-over-day visual, minimal setup Grafana Dashboard (Option 3) Custom growth % calculations App Insights + KQL in Log Analytics (Option 4) Shareable, interactive report Azure Workbooks (Option 5) Per-user/per-agent granularity APIM + App Insights (Option 6) Quick one-off chart, export to Excel Microsoft Foundry Monitor tab or App Insights Metrics Explorer (Option 1 and 2) Option 1. Within the Microsoft Foundry Portal (Quickest, No Setup) If you have models deployed in Microsoft Foundry and would like to monitor its usage, go to the New Foundry Portal → Build → Models → Monitor tab. View metrics such as: Estimated cost Total token usage Input vs. output tokens Number of requests This is the simplest way to monitor both model and agent usage. For PAYG plans: You can also view your total allocated quota (and figure out which Tier you are on) using the Quota Management Screen (New Foundry Portal → Operate → Quota tab). This screen shows how much your total allocated quota is, per model in a given subscription + region + Deployment Type (Global, Data Zones or Regional). For eg., in the image below, for gpt-4o, I am allocated 7M total TPM in my subscription. I am only using 150K TPM of the allocated 7M TPM amount. Which means, my requests will get throttled if I exceed the 150K TPM limit. To avoid throttling, I would need to increase my shared allocation limit. NOTE: you are charged for usage, so if you allow more capacity, you use more, so you pay more. Option 2: Azure Monitor Metrics Explorer This is already built into the Azure Portal and gives you time-series charts out of the box. Go to Azure Portal → your Azure OpenAI / Foundry resource → Monitoring → Metrics Select a metric like AzureOpenAIRequests or TokenTransaction Set Aggregation to Sum (total) or Max and Time granularity to 1 day Split by ModelDeploymentName to see per-model trends Adjust the time range (e.g., last 30 days) — you'll see day-over-day bars/lines Tip: You can pin these charts to an Azure Dashboard for a persistent view, or click Share → Download to Excel to get the raw data for your own analysis. Option 3: Azure Managed Grafana (Best Pre-Built Dashboard) This is the best option for a polished, real-time, day-over-day dashboard with no custom code. There's a pre-built AI Foundry dashboard ready to import. [grafana.com], [Create a M...ed Grafana] How to set it up: Create an Azure Managed Grafana workspace (if you don't have one) In Grafana, go to Dashboards → New → Import → enter dashboard ID 24039 (for Foundry) Select your Azure Monitor data source and point it to your Foundry resource Tip: You can also import this directly from the Azure Portal: Monitor → Dashboards with Grafana → AI Foundry. That's it — the dashboard gives you (per model deployment): Token trends over time (inference, prompt, completion — day over day) Request trends over time (AzureOpenAIRequests as a time series) Latency trends (bonus) NOTE: Default time range is 7 days — adjust to 30/60/90 days for growth trends Option 4: Application Insights + KQL Queries (Most Flexible, Custom Reports) If you want fully custom day-over-day growth calculations (e.g., % change day-to-day), this is the way. [azurefeeds.com] Setup: Ensure your Foundry project is connected to an Application Insights resource (Foundry → Settings → Connected Resources). Open up App Insights resource → Logs → New Query or choose a sample query. In the images below, we simply ran 'requests' and set the time range to 24 hours. There is also a Kusto Query Language (KQL) mode or Simple mode on the right-hand side: Simple mode will let you run out of the box samples. KQL mode will open up a query window for you to enter custom queries. Below are the results in grid view. Same view but showing a chart: Export options: Another way to get the above graphs are via Log Analytics. Simply enable Diagnostic Settings on your Azure OpenAI resource → send to a Log Analytics workspace. Open Log Analytics → Logs and try our your sample queries. Sample KQL for day-over-day token usage (adjust to your needs): AzureMetrics | where MetricName in ("TokenTransaction", "ProcessedPromptTokens", "GeneratedTokens") | where TimeGenerated > ago(30d) | summarize DailyTokens = sum(Total) by bin(TimeGenerated, 1d), MetricName | order by TimeGenerated asc | render timechart Result: Sample KQL for day-over-day growth % (adjust to your needs): AzureMetrics | where MetricName == "TokenTransaction" | where TimeGenerated > ago(30d) | summarize DailyTokens = sum(Total) by Day = bin(TimeGenerated, 1d) | sort by Day asc | extend PrevDay = prev(DailyTokens) | extend GrowthPct = round((DailyTokens - PrevDay) / PrevDay * 100, 2) | project Day, DailyTokens, GrowthPct Option 5: Azure Monitor Workbooks (Custom Dashboards, Shareable) Workbooks let you build interactive, parameterized dashboards that combine metrics and KQL logs. What's more, you can select resources from multiple subscriptions and visualize them all in one place using Workbooks! Go to Azure Portal → Monitor → Workbooks → New Add a Metrics query panel → select your Log Analytics or App Insights or Foundry resource -> Enter the same query you used in Option 4. Do a test run and view the graphs (this can be viewed as charts or a list (grid view)): 4. Save and share with your team. Option 6: APIM + Application Insights (Granular Per-Caller/Per-Agent Tracking) 1. If your app routes requests through Azure API Management, you can use the azure-openai-emit-token-metric policy to send per-request token metrics to Application Insights with custom dimensions (User ID, Subscription ID, Agent, etc.). [Azure API...osoft Docs] This is ideal for scenarios like: "Which agent consumed the most tokens last week?" "What's the token usage per API consumer/team?" NOTE: Microsoft Foundry resources do not track usage by users. So, fronting your Foundry resource with an APIM could be a way to track users provided you pass the username/id in the request context. How you implement this is upto your app design. Ref: AI-Gateway/labs/token-metrics-emitting/token-metrics-emitting.ipynb at main · Azure-Samples/AI-Gateway · GitHub Bonus: Check out all other APIM + AI related policies here: AI-Gateway/labs/semantic-caching at main · Azure-Samples/AI-Gateway AI-Gateway/labs/token-rate-limiting at main · Azure-Samples/AI-Gateway AI-Gateway/labs/token-metrics-emitting/token-metrics-emitting.ipynb at main · Azure-Samples/AI-Gateway · GitHub
