Building a Multi-Agent On-Call Copilot with Microsoft Agent Framework
Four AI agents, one incident payload, structured triage in under 60 seconds powered by Microsoft Agent Framework and Foundry Hosted Agents. Multi-Agent Microsoft Agent Framework Foundry Hosted Agents Python SRE / Incident Response When an incident fires at 3 AM, every second the on-call engineer spends piecing together alerts, logs, and metrics is a second not spent fixing the problem. What if an AI system could ingest the raw incident signals and hand you a structured triage, a Slack update, a stakeholder brief, and a draft post-incident report, all in under 10 seconds? That’s exactly what On-Call Copilot does. In this post, we’ll walk through how we built it using the Microsoft Agent Framework, deployed it as a Foundry Hosted Agent, and discuss the key design decisions that make multi-agent orchestration practical for production workloads. The full source code is open-source on GitHub. You can deploy your own instance with a single azd up . Why Multi-Agent? The Problem with Single-Prompt Triage Early AI incident assistants used a single large prompt: “Here is the incident. Give me root causes, actions, a Slack message, and a post-incident report.” This approach has two fundamental problems: Context overload. A real incident may have 800 lines of logs, 10 alert lines, and dense metrics. Asking one model to process everything and produce four distinct output formats in a single turn pushes token limits and degrades quality. Conflicting concerns. Triage reasoning and communication drafting are cognitively different tasks. A model optimised for structured JSON analysis often produces stilted Slack messages—and vice versa. The fix is specialisation: decompose the task into focused agents, give each agent a narrow instruction set, and run them in parallel. This is the core pattern that the Microsoft Agent Framework makes easy. Architecture: Four Agents Running Concurrently On-Call Copilot is deployed as a Foundry Hosted Agent—a containerised Python service running on Microsoft Foundry’s managed infrastructure. The core orchestrator uses ConcurrentBuilder from the Microsoft Agent Framework SDK to run four specialist agents in parallel via asyncio.gather() . All four panels populated simultaneously: Triage (red), Summary (blue), Comms (green), PIR (purple). Architecture: The orchestrator runs four specialist agents concurrently via asyncio.gather(), then merges their JSON fragments into a single response. All four agents The solution share a single Azure OpenAI Model Router deployment. Rather than hardcoding gpt-4o or gpt-4o-mini , Model Router analyses request complexity and routes automatically. A simple triage prompt costs less; a long post-incident synthesis uses a more capable model. One deployment name, zero model-selection code. Meet the Four Agents 🔍 Triage Agent Root cause analysis, immediate actions, missing data identification, and runbook alignment. suspected_root_causes · immediate_actions · missing_information · runbook_alignment 📋 Summary Agent Concise incident narrative: what happened and current status (ONGOING / MITIGATED / RESOLVED). summary.what_happened · summary.current_status 📢 Comms Agent Audience-appropriate communications: Slack channel update with emoji conventions, plus a non-technical stakeholder brief. comms.slack_update · comms.stakeholder_update 📝 PIR Agent Post-incident report: chronological timeline, quantified customer impact, and specific prevention actions. post_incident_report.timeline · .customer_impact · .prevention_actions The Code: Building the Orchestrator The entry point is remarkably concise. ConcurrentBuilder handles all the async wiring—you just declare the agents and let the framework handle parallelism, error propagation, and response merging. main.py — Orchestrator from agent_framework import ConcurrentBuilder from agent_framework.azure import AzureOpenAIChatClient from azure.ai.agentserver.agentframework import from_agent_framework from azure.identity import DefaultAzureCredential, get_bearer_token_provider from app.agents.triage import TRIAGE_INSTRUCTIONS from app.agents.comms import COMMS_INSTRUCTIONS from app.agents.pir import PIR_INSTRUCTIONS from app.agents.summary import SUMMARY_INSTRUCTIONS _credential = DefaultAzureCredential() _token_provider = get_bearer_token_provider( _credential, "https://cognitiveservices.azure.com/.default" ) def create_workflow_builder(): """Create 4 specialist agents and wire them into a ConcurrentBuilder.""" triage = AzureOpenAIChatClient(ad_token_provider=_token_provider).create_agent( instructions=TRIAGE_INSTRUCTIONS, name="triage-agent", ) summary = AzureOpenAIChatClient(ad_token_provider=_token_provider).create_agent( instructions=SUMMARY_INSTRUCTIONS, name="summary-agent", ) comms = AzureOpenAIChatClient(ad_token_provider=_token_provider).create_agent( instructions=COMMS_INSTRUCTIONS, name="comms-agent", ) pir = AzureOpenAIChatClient(ad_token_provider=_token_provider).create_agent( instructions=PIR_INSTRUCTIONS, name="pir-agent", ) return ConcurrentBuilder().participants([triage, summary, comms, pir]) def main(): builder = create_workflow_builder() from_agent_framework(builder.build).run() # starts on port 8088 if __name__ == "__main__": main() Key insight: DefaultAzureCredential means there are no API keys anywhere in the codebase. The container uses managed identity in production; local development uses your az login session. The same code runs in both environments without modification. Agent Instructions: Prompts as Configuration Each agent receives a tightly scoped system prompt that defines its output schema and guardrails. Here’s the Triage Agent—the most complex of the four: app/agents/triage.py TRIAGE_INSTRUCTIONS = """\ You are the **Triage Agent**, an expert Site Reliability Engineer specialising in root cause analysis and incident response. ## Task Analyse the incident data and return a single JSON object with ONLY these keys: { "suspected_root_causes": [ { "hypothesis": "string – concise root cause hypothesis", "evidence": ["string – supporting evidence from the input"], "confidence": 0.0 // 0-1, how confident you are } ], "immediate_actions": [ { "step": "string – concrete action with runnable command if applicable", "owner_role": "oncall-eng | dba | infra-eng | platform-eng", "priority": "P0 | P1 | P2 | P3" } ], "missing_information": [ { "question": "string – what data is missing", "why_it_matters": "string – why this data would help" } ], "runbook_alignment": { "matched_steps": ["string – runbook steps that match the situation"], "gaps": ["string – gaps or missing runbook coverage"] } } ## Guardrails 1. **No secrets** – redact any credential-like material as [REDACTED]. 2. **No hallucination** – if data is insufficient, set confidence to 0 and add entries to missing_information. 3. **Diagnostic suggestions** – when data is sparse, include diagnostic steps in immediate_actions. 4. **Structured output only** – return ONLY valid JSON, no prose. """ The Comms Agent follows the same pattern but targets a different audience: app/agents/comms.py COMMS_INSTRUCTIONS = """\ You are the **Comms Agent**, an expert incident communications writer. ## Task Return a single JSON object with ONLY this key: { "comms": { "slack_update": "Slack-formatted message with emoji, severity, status, impact, next steps, and ETA", "stakeholder_update": "Non-technical summary for executives. Focus on business impact and resolution." } } ## Guidelines - Slack: Use :rotating_light: for active SEV1/2, :warning: for degraded, :white_check_mark: for resolved. - Stakeholder: No jargon. Translate to business impact. - Tone: Calm, factual, action-oriented. Never blame individuals. - Structured output only – return ONLY valid JSON, no prose. """ Instructions as config, not code. Agent behaviour is defined entirely by instruction text strings. A non-developer can refine agent behaviour by editing the prompt and redeploying no Python changes needed. The Incident Envelope: What Goes In The agent accepts a single JSON envelope. It can come from a monitoring alert webhook, a PagerDuty payload, or a manual CLI invocation: Incident Input (JSON) { "incident_id": "INC-20260217-002", "title": "DB connection pool exhausted — checkout-api degraded", "severity": "SEV1", "timeframe": { "start": "2026-02-17T14:02:00Z", "end": null }, "alerts": [ { "name": "DatabaseConnectionPoolNearLimit", "description": "Connection pool at 99.7% on orders-db-primary", "timestamp": "2026-02-17T14:03:00Z" } ], "logs": [ { "source": "order-worker", "lines": [ "ERROR: connection timeout after 30s (attempt 3/3)", "WARN: pool exhausted, queueing request (queue_depth=847)" ] } ], "metrics": [ { "name": "db_connection_pool_utilization_pct", "window": "5m", "values_summary": "Jumped from 22% to 99.7% at 14:03Z" } ], "runbook_excerpt": "Step 1: Check DB connection dashboard...", "constraints": { "max_time_minutes": 15, "environment": "production", "region": "swedencentral" } } Declaring the Hosted Agent The agent is registered with Microsoft Foundry via a declarative agent.yaml file. This tells Foundry how to discover and route requests to the container: agent.yaml kind: hosted name: oncall-copilot description: | Multi-agent hosted agent that ingests incident signals and runs 4 specialist agents concurrently via Microsoft Agent Framework ConcurrentBuilder. metadata: tags: - Azure AI AgentServer - Microsoft Agent Framework - Multi-Agent - Model Router protocols: - protocol: responses environment_variables: - name: AZURE_OPENAI_ENDPOINT value: ${AZURE_OPENAI_ENDPOINT} - name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME value: model-router The protocols: [responses] declaration exposes the agent via the Foundry Responses API on port 8088. Clients can invoke it with a standard HTTP POST no custom API needed. Invoking the Agent Once deployed, you can invoke the agent with the project’s built-in scripts or directly via curl : CLI / curl # Using the included invoke script python scripts/invoke.py --demo 2 # multi-signal SEV1 demo python scripts/invoke.py --scenario 1 # Redis cluster outage # Or with curl directly TOKEN=$(az account get-access-token \ --resource https://ai.azure.com --query accessToken -o tsv) curl -X POST \ "$AZURE_AI_PROJECT_ENDPOINT/openai/responses?api-version=2025-05-15-preview" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "input": [ {"role": "user", "content": "<incident JSON here>"} ], "agent": { "type": "agent_reference", "name": "oncall-copilot" } }' The Browser UI The project includes a zero-dependency browser UI built with plain HTML, CSS, and vanilla JavaScript—no React, no bundler. A Python http.server backend proxies requests to the Foundry endpoint. The empty state. Quick-load buttons pre-populate the JSON editor with demo incidents or scenario files. Demo 1 loaded: API Gateway 5xx spike, SEV3. The JSON is fully editable before submitting. Agent Output Panels Triage: Root causes ranked by confidence. Evidence is collapsed under each hypothesis. Triage: Immediate actions with P0/P1/P2 priority badges and owner roles. Comms: Slack card with emoji substitution and a stakeholder executive summary. PIR: Chronological timeline with an ONGOING marker, customer impact in a red-bordered box. Performance: Parallel Execution Matters Incident Type Complexity Parallel Latency Sequential (est.) Single alert, minimal context (SEV4) Low 4–6 s ~16 s Multi-signal, logs + metrics (SEV2) Medium 7–10 s ~28 s Full SEV1 with long log lines High 10–15 s ~40 s Post-incident synthesis (resolved) High 10–14 s ~38 s asyncio.gather() running four independent agents cuts total latency by 3–4× compared to sequential execution. For a SEV1 at 3 AM, that’s the difference between a 10-second AI-powered head start and a 40-second wait. Five Key Design Decisions Parallel over sequential Each agent is independent and processes the full incident payload in isolation. ConcurrentBuilder with asyncio.gather() is the right primitive—no inter-agent dependencies, no shared state. JSON-only agent instructions Every agent returns only valid JSON with a defined schema. The orchestrator merges fragments with merged.update(agent_output) . No parsing, no extraction, no post-processing. No hardcoded model names AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=model-router is the only model reference. Model Router selects the best model at runtime based on prompt complexity. When new models ship, the agent gets better for free. DefaultAzureCredential everywhere No API keys. No token management code. Managed identity in production, az login in development. Same code, both environments. Instructions as configuration Each agent’s system prompt is a plain Python string. Behaviour changes are text edits, not code logic. A non-developer can refine prompts and redeploy. Guardrails: Built into the Prompts The agent instructions include explicit guardrails that don’t require external filtering: No hallucination: When data is insufficient, the agent sets confidence: 0 and populates missing_information rather than inventing facts. Secret redaction: Each agent is instructed to redact credential-like patterns as [REDACTED] in its output. Mark unknowns: Undeterminable fields use the literal string "UNKNOWN" rather than plausible-sounding guesses. Diagnostic suggestions: When signal is sparse, immediate_actions includes diagnostic steps that gather missing information before prescribing a fix. Model Router: Automatic Model Selection One of the most powerful aspects of this architecture is Model Router. Instead of choosing between gpt-4o , gpt-4o-mini , or o3-mini per agent, you deploy a single model-router endpoint. Model Router analyses each request’s complexity and routes it to the most cost-effective model that can handle it. Model Router insights: models selected per request with associated costs. Model Router telemetry from Microsoft Foundry: request distribution and cost analysis. This means you get optimal cost-performance without writing any model-selection logic. A simple Summary Agent prompt may route to gpt-4o-mini , while a complex Triage Agent prompt with 800 lines of logs routes to gpt-4o all automatically. Deployment: One Command The repo includes both azure.yaml and agent.yaml , so deployment is a single command: Deploy to Foundry # Deploy everything: infra + container + Model Router + Hosted Agent azd up This provisions the Foundry project resources, builds the Docker image, pushes to Azure Container Registry, deploys a Model Router instance, and creates the Hosted Agent. For more control, you can use the SDK deploy script: Manual Docker + SDK deploy # Build and push (must be linux/amd64) docker build --platform linux/amd64 -t oncall-copilot:v1 . docker tag oncall-copilot:v1 $ACR_IMAGE docker push $ACR_IMAGE # Create the hosted agent python scripts/deploy_sdk.py Getting Started Quickstart # Clone git clone https://github.com/microsoft-foundry/oncall-copilot cd oncall-copilot # Install python -m venv .venv source .venv/bin/activate # .venv\Scripts\activate on Windows pip install -r requirements.txt # Set environment variables export AZURE_OPENAI_ENDPOINT="https://<account>.openai.azure.com/" export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="model-router" export AZURE_AI_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>" # Validate schemas locally (no Azure needed) MOCK_MODE=true python scripts/validate.py # Deploy to Foundry azd up # Invoke the deployed agent python scripts/invoke.py --demo 1 # Start the browser UI python ui/server.py # → http://localhost:7860 Extending: Add Your Own Agent Adding a fifth agent is straightforward. Follow this pattern: Create app/agents/<name>.py with a *_INSTRUCTIONS constant following the existing pattern. Add the agent’s output keys to app/schemas.py . Register it in main.py : main.py — Adding a 5th agent from app.agents.my_new_agent import NEW_INSTRUCTIONS new_agent = AzureOpenAIChatClient( ad_token_provider=_token_provider ).create_agent( instructions=NEW_INSTRUCTIONS, name="new-agent", ) workflow = ConcurrentBuilder().participants( [triage, summary, comms, pir, new_agent] ) Ideas for extensions: a ticket auto-creation agent that creates Jira or Azure DevOps items from the PIR output, a webhook adapter agent that normalises PagerDuty or Datadog payloads, or a human-in-the-loop agent that surfaces missing_information as an interactive form. Key Takeaways for AI Engineers The multi-agent pattern isn’t just for chatbots. Any task that can be decomposed into independent subtasks with distinct output schemas is a candidate. Incident response, document processing, code review, data pipeline validation—the pattern transfers. Microsoft Agent Framework gives you ConcurrentBuilder for parallel execution and AzureOpenAIChatClient for Azure-native auth—you write the prompts, the framework handles the plumbing. Foundry Hosted Agents let you deploy containerised agents with managed infrastructure, automatic scaling, and built-in telemetry. No Kubernetes, no custom API gateway. Model Router eliminates the model selection problem. One deployment name handles all scenarios with optimal cost-performance tradeoffs. Prompt-as-config means your agents are iterable by anyone who can edit text. The feedback loop from “this output could be better” to “deployed improvement” is minutes, not sprints. Resources Microsoft Agent Framework SDK powering the multi-agent orchestration Model Router Automatic model selection based on prompt complexity Foundry Hosted Agents Deploy containerised agents on managed infrastructure ConcurrentBuilder Samples Official agents-in-workflow sample this project follows DefaultAzureCredential Zero-config auth chain used throughout Hosted Agents Concepts Architecture overview of Foundry Hosted Agents The On-Call Copilot sample is open source under the MIT licence. Contributions, scenario files, and agent instruction improvements are welcome via pull request.
Getting Started with Behave: Writing Cucumber Tests in VS Code
What is Behave? Behave is a BDD test framework for Python that allows you to write tests in plain English using Given–When–Then syntax, backed by Python step definitions. Key benefits: Human‑readable test scenarios using Gherkin Strong alignment between business requirements and test automation Easy integration with CI/CD pipelines Lightweight and IDE‑friendly Prerequisites Before getting started, ensure you have the following installed: Python 3.10+ Visual Studio Code Basic understanding of Python Familiarity with BDD concepts (Given / When / Then) Steps Download the sample demo zip from github download Step 1: Create a Virtual Environment and activate it. python -m venv venv .venv\Scripts\activate Install Dependencies pip install behave requests Step 2: Install VS Code Extensions To get a first‑class experience in VS Code, install the following extensions: Python (Microsoft) Gherkin (for .feature syntax highlighting) Behave VSC (optional but recommended) The Behave VSC extension enables: Running tests directly from VS Code Step definition navigation Gherkin auto‑completion Test explorer integration Folder Structure Why This Structure? features/ – contains all Gherkin feature files steps/ – contains Python step implementations environment.py – optional hooks for setup/teardown config/configuration.py - for lifecycle hooks behave.ini – configuration file for Behave Step 3: Write Your First Feature File Feature: Login functionality Login Scenario: Successful login Given the application is running When the user enters valid credentials Then the user should see the dashboard Step 4: Writing Step Definitions from behave import given, when, then @given('the user is on the login page') def step_user_on_login_page(context): print("User navigates to login page") @when('the user enters valid credentials') def step_user_enters_credentials(context): print("User enters username and password") @then('the user should be redirected to the dashboard') def step_user_redirected(context): print("User is redirected to dashboard") Step 5: Adding Test Configuration (configuration.py) Create config/configuration.py to centralize environment-specific settings. This helps avoid hardcoding values across test files. class TestConfig: BASE_URL = "https://example.com" TIMEOUT = 30 BROWSER = "chrome" Step 6: Using Fixtures with environment.py The environment.py file is Behave’s hook mechanism. It runs before and after tests, similar to fixtures in pytest. Create features/environment.py: from config.configuration import TestConfig def before_all(context): print("Setting up test environment") context.config_data = TestConfig() def before_scenario(context, scenario): print(f"Starting scenario: {scenario.name}") def after_scenario(context, scenario): print(f"Finished scenario: {scenario.name}") def after_all(context): print("Tearing down test environment") Common Use Cases Initialize browsers or API clients Load environment variables Clean up test data Open/close DB connections Step 7: Optional Behave Configuration File Create behave.ini for execution settings. This helps during debugging by showing logs directly in the console. [behave] stdout_capture = false stderr_capture = false log_capture = false Step 8: Running Tests From the project root, run: behave To run a specific feature: behave features/login.feature Run by tag behave -t Login Best Practices ✔ Keep feature files business-readable ✔ Avoid logic in feature files ✔ Reuse steps wherever possible ✔ Centralize configs and fixtures ✔ Use tags for selective executionMicrosoft Agent Framework, Microsoft Foundry, MCP, Aspire を使った実践的な AI アプリを構築するサンプルが登場
AI エージェントを作ること自体は、以前よりも簡単になってきました。しかし、それらを実際の本番運用のアプリケーションの一部としてデプロイすること (複数のサービス、永続的な状態管理、本番向けのインフラを含めた形で運用すること) になると、途端に複雑になります。 .NET コミュニティの開発者からも、ローカル環境でもクラウドでも動作する、クラウドネイティブな実運用レベルのサンプルが見たいという声が多く寄せられていました。 その声に応え、私たちはオープンソースのサンプルアプリ『Interview Coach (面接コーチ)』を作りました。模擬就職面接を行う AI チャット web アプリです。 このサンプルでは、本番運用を想定したサービスにおいて、以下の技術がどのように組み合わさるのかを示しています： Microsoft Agent Framework Microsoft Foundry Model Context Protocol (MCP) Aspire このアプリは、実際に動作する 面接シミュレーター です。AI コーチがユーザーに対して行動面や技術面の質問を行い、最後に面接パフォーマンスのまとめをフィードバックとして提供します。 この記事では、このアプリで使用している設計パターンと、それらがどのような課題を解決するのかを紹介します。 こちらから Interview Coach デモアプリ を試すことができます。 なぜ Microsoft Agent Framework なのか？ もしこれまで .NET で AI エージェントを開発してきたなら、おそらく Semantic Kernel や AutoGen、あるいはその両方を使ったことがあるでしょう。 Microsoft Agent Framework は、それらの次のステップにあたるフレームワークです。 このフレームワークは、同じチームによって開発されており、両プロジェクトをうまく統合して、1つのフレームワークにまとめたものです。 具体的には、 AutoGen のエージェント抽象化 Semantic Kernel のエンタープライズ機能 （状態管理、型安全性、ミドルウェア、テレメトリーなど） を統合し、さらにマルチエージェントのオーケストレーションのためのグラフベースのワークフローを追加しています。 .NET 開発者にとってのメリットは次のとおりです： フレームワークが1つに統合： Semantic Kernel と AutoGen のどちらを使うか悩む必要がありません。 馴染みのある開発パターン： エージェントは dependency injection、IChatClient、そして ASP.NET アプリと同じホスティングモデルを利用します。 本番運用を前提とした設計： OpenTelemetry、ミドルウェアパイプライン、Aspire との統合が最初から用意されています。 マルチエージェントのオーケストレーション： 逐次ワークフロー、並列実行、handoff パターン、グループチャットなどをサポートします。 Interview Coach は、これらの機能を単なる Hello World ではなく、実際のアプリケーションとしてまとめたサンプルです。 なぜ Microsoft Foundry なのか？ AI エージェントには、単にモデルがあれば良いわけではありません。インフラも必要です。 Microsoft Foundry は、AI アプリケーションを構築・管理するための Azure のプラットフォームであり、Microsoft Agent Framework の推奨バックエンドでもあります。 Foundry を使うと、次のような機能を1つのポータルで利用できます： モデルアクセス： OpenAI、Meta、Mistral などのモデルを1つのエンドポイントから利用できるカタログ Content safety (安全性)： モデレーションや個人情報（PII）検出が組み込まれており、エージェントが問題のある出力をしないように制御。 コスト最適化ルーティング： リクエストがタスクに最適なモデルへ自動的にルーティングされる 評価とファインチューニング： エージェントの品質を測定し、継続的に改善できる エンタープライズ向けガバナンス： Entra ID や Microsoft Defender による認証、アクセス制御、コンプライアンス Interview Coach では、Foundry がエージェントを動かすモデルエンドポイントを提供しています。 エージェントコードは IChatClient インターフェースを利用しているため、Foundry はあくまで設定の選択肢の 1 つですが、最初から豊富なツールが揃っている点で最も便利な選択肢です。 Interview Coach は何をするアプリなのか？ Interview Coach は、模擬就職面接を行う対話型 AI です。 ユーザーが 履歴書（resume） と 応募先の職務内容（job description）を入力すると、そこから先はエージェントが面接プロセスを進めていきます。 情報収集（Intake）： 履歴書と応募先の職務内容を収集します。 行動面接（Behavioral interview）： あなたの経験に合わせて、 STAR メソッド (過去の行動を構造的に説明するための回答フレームワークで、Situation, Task, Action, Result の頭文字から来ている) に基づいた質問を行います。 技術面接（Technical interview）： 応募する職種に応じた技術的な質問を行います。 まとめ（Summary）： 面接のパフォーマンス (成績) を評価し、具体的なフィードバックを含むレビューを生成します。 ユーザーは、このシステムと Blazor の Web UI を通して対話します。 AI の回答は リアルタイムでストリーミング表示されます。 余談： Behavioral Interview とは Behavioral Interview（行動面接/行動事例面接）とは、応募者の「過去の具体的な行動」を深掘りし、その人の行動特性、スキル、考え方が企業の求める人材像と適合しているかを判断する面接手法です。 単なる知識や志望動機ではなく、「ストレスを感じた時どう対処したか」など過去の事実に基づき、将来のパフォーマンスを予測します。 アーキテクチャ概要 このアプリケーションは、複数のサービスに分割されており、すべて Aspire によってオーケストレーションされています： LLM Provider： Microsoft Foundry（推奨）を利用し、さまざまなモデルへアクセスします。 WebUI： 面接の対話を行うための Blazor ベースのチャットインターフェースです。 Agent： 面接のロジックを担うコンポーネントで、Microsoft Agent Framework 上で構築されています。 MarkItDown MCP Server： Microsoft の MarkItDown (なんでも Markdown にしてくれる Python ライブラリ) を利用し、履歴書（PDF や DOCX）を Markdown 形式に変換して解析します。 InterviewData MCP Server： .NET で実装された MCP サーバーで、面接セッションのデータを SQLite に保存します。 Aspire は、サービスディスカバリ、ヘルスチェック、テレメトリーを管理します。 各コンポーネントは 独立したプロセスとして実行され、1 つのコマンドでアプリケーション全体を起動できます。 パターン 1 : マルチエージェントによるハンドオフ このサンプルが特に興味深いのは、 ハンドオフ （handoff） パターン を採用している点です。 1 つのエージェントがすべてを処理するのではなく、面接のプロセスを 5 つの専門エージェントに分割しています： Agent 役割 Tools Triage (トリアージ) メッセージを適切な担当エージェントへ振り分ける なし（ルーティングのみ） Receptionist (受付) セッションを作成し、履歴書と職務内容を収集 MarkItDown + InterviewData Behavioral Interviewer (行動面接官) STAR メソッドを用いた行動面接を実施 InterviewData Technical Interviewer (技術面接官) 職種に応じた技術面接の質問を行う InterviewData Summarizer (サマリー生成) 面接の最終的なサマリーを生成 InterviewData ハンドオフパターンでは、あるエージェントが会話の制御を次のエージェントに完全に引き渡します。 引き継いだエージェントが、その後の会話をすべて担当します。 これは 「agent-as-tools」パターンとは異なります。 (agent-as-tools では、メインのエージェントが他のエージェントを補助ツールとして呼び出しますが、会話の制御自体はメインエージェントが保持します。) 以下は、このハンドオフワークフローの構成例です： var workflow = AgentWorkflowBuilder .CreateHandoffBuilderWith(triageAgent) .WithHandoffs(triageAgent, [receptionistAgent, behaviouralAgent, technicalAgent, summariserAgent]) .WithHandoffs(receptionistAgent, [behaviouralAgent, triageAgent]) .WithHandoffs(behaviouralAgent, [technicalAgent, triageAgent]) .WithHandoffs(technicalAgent, [summariserAgent, triageAgent]) .WithHandoff(summariserAgent, triageAgent) .Build(); 通常の処理フロー（happy path）は次の順序で進みます。 Receptionist → Behavioral → Technical → Summarizer それぞれの専門エージェントが、次のエージェントへ直接ハンドオフします。 もし想定外の状況が発生した場合は、エージェントは Triage エージェントへ戻り、適切なルーティングを再度行います。 なお、このサンプルには 単一エージェントモードも用意されており、よりシンプルな構成でのデプロイも可能です。 これにより、単一エージェントとマルチエージェントのアプローチを比較することができます。 パターン2: ツール統合のための MCP このプロジェクトでは、ツールはエージェントの内部に実装されていません。 それぞれが 独立した MCP（Model Context Protocol）サーバーとして提供されています。 例えば、MarkItDown サーバーはこのプロジェクトだけでなく、まったく別のエージェントプロジェクトでも再利用できます。また、ツール開発チームはエージェント開発チームとは独立してツールをリリースすることが可能です。 MCP は言語非依存（language-agnostic）であることも特徴です。 そのため、このサンプルでは MarkItDown が Python サーバーとして動作し、エージェントは .NET で実装されています。 エージェントは起動時に MCP クライアントを通じてツールを検出し、必要なエージェントにそれらを渡します。 var receptionistAgent = new ChatClientAgent( chatClient: chatClient, name: "receptionist", instructions: "You are the Receptionist. Set up sessions and collect documents...", tools: [.. markitdownTools, .. interviewDataTools]); 各エージェントには、必要なツールだけが割り当てられます： Triage エージェント：ツールなし（ルーティングのみを担当） インタビュアーエージェント：セッションデータへのアクセス Receptionist エージェント：ドキュメント解析 + セッションアクセス これは 最小権限の原則（principle of least privilege） に基づいた設計です。 パターン3: Aspire によるオーケストレーション Aspire は、アプリケーション全体をまとめて管理する役割を担います。 アプリホストはサービスのトポロジー（構成）を定義し、 どのサービスが存在するのか それぞれがどのように依存しているのか どの設定を受け取るのか を管理します。 これにより、次のような機能が利用できます： Service discovery. サービスは固定の URL ではなく、サービス名で互いを見つけることができます。 Health checks. Aspire ダッシュボードで、各コンポーネントの状態を確認できます。 Distributed tracing. 共通のサービス設定を通じて OpenTelemetry が組み込まれます。 One-command startup. aspire run --file ./apphost.cs を実行するだけで、すべてのサービスが起動します。 デプロイ時には、azd up を実行することで、アプリケーション全体が Azure Container Apps にデプロイされます。 始めてみよう 事前準備 .NET 10 SDK 以降 Azure サブスクリプション Microsoft Foundry project Docker Desktop またはその他のコンテナランタイム ローカルで実行する git clone https://github.com/Azure-Samples/interview-coach-agent-framework.git cd interview-coach-agent-framework # Configure credentials dotnet user-secrets --file ./apphost.cs set MicrosoftFoundry:Project:Endpoint "<your-endpoint>" dotnet user-secrets --file ./apphost.cs set MicrosoftFoundry:Project:ApiKey "<your-key>" # Start all services aspire run --file ./apphost.cs Aspire Dashboard を開き、すべてのサービスの状態が Running になるまで待ちます。 その後、WebUI のエンドポイントをクリックすると、模擬面接を開始できます。 以下は、ハンドオフパターンがどのように動作するかを DevUI 上で可視化したものです。 このチャット UI を使って、面接候補者としてエージェントと対話することができます。 Azure にデプロイする azd auth login azd up Tこれだけで完了です。 残りの処理は Aspire と azd が自動で実行します。 デプロイとテストが完了したら、次のコマンドを実行することで、作成されたすべてのリソースを安全に削除できます。 azd down --force --purge このサンプルから学べること Interview Coach を実際に試すことで、次のような内容を理解できます： Microsoft Foundry を モデルバックエンドとして利用する方法 Microsoft Agent Framework を使った 単一エージェントおよびマルチエージェントシステムの構築 ハンドオフによるオーケストレーションを用いて、ワークフローを専門エージェントに分割する方法 エージェントコードとは独立した MCP ツールサーバーの作成と利用 Aspire を使った 複数サービスからなるアプリケーションのオーケストレーション 一貫性のある構造化された振る舞いを生み出すプロンプト設計 azd up を使った アプリケーション全体のデプロイ方法 試してみよう 完全なソースコードは GitHub で公開されています： Azure-Samples/interview-coach-agent-framework Microsoft Agent Framework を初めて使う場合は、まず次の資料から始めることをおすすめします。 framework documentation Hello World sample. その後、このサンプルに戻ってくると、これらの要素がより大きなプロジェクトの中でどのように組み合わさるのかが理解できるでしょう。 もしこれらのパターンを使って何か作った場合は、ぜひ Issue を作成して 教えてください。 次は？ (What's Next?) 我々は、現在、次のような さらなる統合シナリオにも取り組んでいます： Microsoft Foundry Agent Service GitHub Copilot A2A などなど。 これらの機能がリリースされ次第、このサンプルも随時アップデートしていく予定です。 Resources Microsoft Agent Framework ドキュメント Introducing Microsoft Agent Framework preview Microsoft Agent Framework Reaches Release Candidate Microsoft Foundry ドキュメント Microsoft Foundry Agent Service Microsoft Foundry Portal Microsoft.Extensions.AI Model Context Protocol specification Aspire ドキュメント ASP.NET BlazorPhi-4-Reasoning-Vision-15B: Use Cases In-Depth
Phi-4-Reasoning-vision-15B is Microsoft's latest vision reasoning model released on Microsoft Foundry. It combines high-resolution visual perception with selective, task-aware reasoning, making it the first model in the Phi-4 family to simultaneously achieve both "seeing clearly" and "thinking deeply" as a small language model (SLM). Traditional vision models only perform passive perception — recognizing "what's in" an image. Phi-4-Reasoning-Vision-15B goes further by performing structured, multi-step reasoning: understanding visual structure in images, connecting it with textual context, and reaching actionable conclusions. This enables developers to build intelligent applications ranging from chart analysis to GUI automation. Core Design Features 2.1 Selective Reasoning The model's most critical design feature is its hybrid reasoning behavior. It can switch between "reasoning mode" and "non-reasoning mode" based on the prompt: When deep reasoning is needed (e.g., math problems, logical analysis) → Multi-step reasoning chain is activated When fast perception is sufficient (e.g., OCR, element localization) → Direct output with reduced latency 2.2 Three Thinking Modes (from Notebook Examples) Developers can precisely control reasoning behavior via the thinking_mode parameter: Mode Trigger Description Best For hybrid (Mixed) Default Model autonomously decides whether deep reasoning is needed General use, balancing speed and accuracy think (Deep Thinking) Appends <think> token Forces full reasoning chain Complex math / science / logic problems nothink (Fast Response) Appends <nothink> token Skips reasoning chain, outputs directly Low-latency perception tasks, simple Q&A The corresponding code implementation: def run_inference(processor, model, prompt, image, thinking_mode="hybrid"): ## FORM MESSAGE AND LOAD IMAGE messages = [ { "role": "user", "content": prompt, } ] ## PROCESS INPUTS prompt = processor.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, return_dict=False, ) if thinking_mode == "think": prompt = str(prompt) + "<think>" elif thinking_mode == "nothink": prompt = str(prompt) + "<|dummy_84|>" print(f"Prompt: {prompt}") inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device) ## GENERATE RESPONSE output_ids = model.generate( **inputs, max_new_tokens=1024, temperature=None, top_p=None, do_sample=False, use_cache=False, ) ## DECODE RESPONSE sequence_length = inputs["input_ids"].shape[1] sequence_length -= 1 if thinking_mode == "think" else 0 # remove the extra token for nothink mode new_output_ids = output_ids[:, sequence_length:] model_output = processor.batch_decode( new_output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] return model_output This design allows developers to dynamically balance latency and accuracy at runtime — essential for real-time interactive applications. Key Use Cases Use Case 1: GUI Agents (Computer Use Agents) This is one of the model's most important application areas.The model receives a screenshot and a natural language instruction, then outputs the normalized bounding box coordinates for the target UI element. The Notebook also provides a plot_boxes() visualization function that compares model predictions (red box) against ground truth annotations (green box). Real-World Example — E-Commerce Shopping Agent: As described in the official documentation, in retail scenarios the model serves as the perception layer for computer-use agents: Screen comprehension: Identifies products, prices, filters, promotions, buttons, and cart states Grounded output: Produces actionable coordinates for upstream agent models (e.g., Fara-7B) to execute clicks, scrolls, and other interactions Real-time decision support: Compact model size and low-latency inference, suitable for navigating dense product listings and comparing options Use Case 2: Mathematical and Scientific Visual Reasoning Typical applications: Interpreting geometric figures and function graphs for problem-solving Analyzing scientific experiment diagrams and data charts Education: Students photograph and upload problems; the model shows the complete reasoning process and solution steps Use Case 3: Document, Chart, and Table Understanding Typical applications: IT Operations: Interpreting monitoring dashboards, performance charts, and incident reports to assist diagnosis and decision-making Financial Analysis: Extracting metrics from report screenshots and interpreting trends Enterprise Report Automation: Processing scanned documents and tables to generate structured summaries Samples 1. Using Phi-4-Reasoning-Vision-15B to detect jaywalking Go to - Sample Code 2. Using Phi-4-Reasoning-Vision-15B to math Go to - Sample Code 3. Using Phi-4-Reasoning-Vision-15B for GUI Agent Go to - Sample Code Model Comparison at a Glance Below is a comparison of Phi-4-Reasoning-Vision-15B against comparable models on key tasks: No Thinking Mode Thinking Mode Phi-4-Reasoning-Vision-15B shows clear advantages in math reasoning and GUI grounding tasks while remaining competitive in general multimodal understanding. Summary Phi-4-Reasoning-Vision-15B represents a significant milestone for small vision reasoning models: Sees clearly: High-resolution visual perception supporting documents, charts, UI screenshots, and more Thinks deeply: Selective multi-step reasoning chains that rival larger models on complex tasks Runs fast: 15B parameters + NoThink mode, suitable for real-time interactive applications Adapts flexibly: Three thinking modes switchable on the fly, letting developers dynamically balance accuracy and latency at runtime Whether building e-commerce shopping agents, IT operations assistants, or educational tutoring tools, this model provides a complete capability chain from "seeing" to "understanding" to "acting." Resources 1. Read official Blog - Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model 2. Learn more about Phi-4-reasoning-vision in Huggingface - https://huggingface.co/microsoft/Phi-4-reasoning-vision-15B 3. Learn more about Microsoft Phi Family - Microsoft Phi CookBookGiving Your AI Agents Reliable Skills with the Agent Skills SDK
AI agents are becoming increasingly capable, but they often do not have the context they need to do real work reliably. Your agent can reason well, but it does not actually know how to do the specific things your team needs it to do. For example, it cannot follow your company's incident response playbook, it does not know your escalation policy, and it has no idea how to page the on-call engineer at 3 AM. There are many ways to close this gap, from RAG to custom tool implementations. Agent Skills is one approach that stands out because it is designed around portability and progressive disclosure, keeping context window usage minimal while giving agents access to deep expertise on demand. What is Agent Skills? Agent Skills is an open format for giving agents new capabilities and expertise. The format was originally developed by Anthropic and released as an open standard. It is now supported by a growing list of agent products including Claude Code, VS Code, GitHub, OpenAI Codex, Cursor, Gemini CLI, and many others. As defined in the spec, a skill is a folder on disk containing a SKILL.md file with metadata and instructions, plus optional scripts, references, and assets: incident-response/ SKILL.md # Required: instructions + metadata references/ # Optional: additional documentation severity-levels.md escalation-policy.md scripts/ # Optional: executable code page-oncall.sh assets/ # Optional: templates, diagrams, data files The SKILL.md file has YAML frontmatter with a name and description (so agents know when the skill is relevant), followed by markdown instructions that tell the agent how to perform the task. The format is intentionally simple: self-documenting, extensible, and portable. What makes this design practical is progressive disclosure. The spec is built around the idea that agents should not load everything at once. It works in three stages: Discovery: At startup, agents load only the name and description of each available skill, just enough to know when it might be relevant. Activation: When a task matches a skill's description, the agent reads the full SKILL.md instructions into context. Execution: The agent follows the instructions, optionally loading referenced files or executing bundled scripts as needed. This keeps agents fast while giving them access to deep context on demand. The format is well-designed and widely adopted, but if you want to use skills from your own agents, there is a gap between the spec and a working implementation. The Agent Skills SDK Conceptually, a skill is more than a folder. It is a unit of expertise: a name, a description, a body of instructions, and a set of supporting resources. The file layout is one way to represent that, but there is nothing about the concept that requires a filesystem. The Agent Skills SDK is an open-source Python library built around that idea, treating skills as abstract units of expertise that can be stored anywhere and consumed by any agent framework. It does this by addressing two challenges that come up when you try to use the format from your own agents. The first is where skills live. The spec defines skills as folders on disk, and the tools that support the format today all assume skills are local files. Files are inherently portable, and that is one of the format's strengths. But in the real world, not every team can or wants to serve skills from the filesystem. Maybe your team keeps them in an S3 bucket. Maybe they are in Azure Blob Storage behind your CDN. Maybe they live in a database alongside the rest of your application data. At the moment, if your skills are not on the local filesystem, you are on your own. The SDK changes where skills are served from, not how they are authored. The content and format stay the same regardless of the storage backend, so skills remain portable across providers. The second is how agents consume them. The spec defines the progressive disclosure pattern but actually implementing it in your agent requires real work. You need to figure out how to validate skills against the spec, generate a catalog for the system prompt, expose the right tools for on-demand content retrieval, and handle the back-and-forth of the agent requesting metadata, then the body, then individual references or scripts. That is a lot of plumbing regardless of where the skills are stored, and the work multiplies if you want to support more than one agent framework. The SDK solves both by separating where skills come from (providers) from how agents use them (integrations), so you can mix and match freely. Load skills from the filesystem today, move them to an HTTP server tomorrow, swap in a custom database provider next month, and your agent code does not change at all. How the SDK works The SDK is a set of Python packages organized around two ideas: storage-agnostic providers and progressive disclosure. The provider abstraction means your skills can live anywhere. The SDK ships with providers for the local filesystem and static HTTP servers, but the SkillProvider interface is simple enough that you can write your own in a few methods. A Cosmos DB provider, a Git provider, a SharePoint provider, whatever makes sense for your team. The rest of the SDK does not care where the data comes from. On top of that, the SDK implements the progressive disclosure pattern from the spec as a set of tools that any LLM agent can use. At startup, the SDK generates a skills catalog containing each skill's name and description. Your agent injects this catalog into its system prompt so it knows what is available. Then, during a conversation, the agent calls tools to retrieve content on demand, following the same discovery-activation-execution flow the spec describes. Here is the flow in practice: You register skills from any source (local files, an HTTP server, your own database). The SDK generates a catalog and tool usage instructions, which you inject into the system prompt. The agent calls tools to retrieve content on demand. This matters because context windows are finite. An incident response skill might have a main body, three reference documents, two scripts, and a flowchart. The agent should not load all of that upfront. It should read the body first, then pull the escalation policy only when the conversation actually gets to escalation. A quick example Here is what it looks like in practice. Start by loading a skill from the filesystem: from pathlib import Path from agentskills_core import SkillRegistry from agentskills_fs import LocalFileSystemSkillProvider provider = LocalFileSystemSkillProvider(Path("my-skills")) registry = SkillRegistry() await registry.register("incident-response", provider) Now wire it into a LangChain agent: from langchain.agents import create_agent from agentskills_langchain import get_tools, get_tools_usage_instructions tools = get_tools(registry) skills_catalog = await registry.get_skills_catalog(format="xml") tool_usage_instructions = get_tools_usage_instructions() system_prompt = ( "You are an SRE assistant. Use the available skill tools to look up " "incident response procedures, severity definitions, and escalation " "policies. Always cite which reference document you used.\n\n" f"{skills_catalog}\n\n" f"{tool_usage_instructions}" ) agent = create_agent( llm, tools, system_prompt=system_prompt, ) That is it. The agent now knows what skills are available and has tools to fetch their content. When a user asks "How do I handle a SEV1 incident?", the agent will call get_skill_body to read the instructions, then get_skill_reference to pull the severity levels document, all without you writing any of that retrieval logic. The same pattern works with Microsoft Agent Framework: from agentskills_agentframework import get_tools, get_tools_usage_instructions tools = get_tools(registry) skills_catalog = await registry.get_skills_catalog(format="xml") tool_usage_instructions = get_tools_usage_instructions() system_prompt = ( "You are an SRE assistant. Use the available skill tools to look up " "incident response procedures, severity definitions, and escalation " "policies. Always cite which reference document you used.\n\n" f"{skills_catalog}\n\n" f"{tool_usage_instructions}" ) agent = Agent( client=client, instructions=system_prompt, tools=tools, ) What is in the SDK The SDK is split into small, composable packages so you only install what you need: agentskills-core handles registration, validation, the skills catalog, and the progressive disclosure API. It also defines the SkillProvider interface that all providers implement. agentskills-fs and agentskills-http are the two built-in providers. The filesystem provider loads skills from local directories. The HTTP provider loads them from any static file host: S3, Azure Blob Storage, GitHub Pages, a CDN, or anything that serves files over HTTP. agentskills-langchain and agentskills-agentframework generate framework-native tools and tool usage instructions from a skill registry. agentskills-mcp-server spins up an MCP server that exposes skill tool access and usage as tools and resources, so any MCP-compatible client can use them. Because providers and integrations are separate packages, you can combine them however you want. Use the filesystem provider during development, switch to the HTTP provider in production, or write a custom provider that reads skills from your own database. The integration layer does not need to know or care. Where to go from here The full source, working examples, and detailed API docs are on GitHub: github.com/pratikxpanda/agentskills-sdk The repo includes end-to-end examples for both LangChain and Microsoft Agent Framework, covering filesystem providers, HTTP providers, and MCP. There is also a sample incident-response skill you can use to try things out. A proposal to contribute this SDK to the official agentskills repository has been submitted. If you find it useful, feel free to show your support on the GitHub issue. To learn more about the Agent Skills format itself: What are skills? covers the format and why it matters. Specification is the complete format reference for SKILL.md files. Integrate skills explains how to add skills support to your agent. Example skills on GitHub are a good starting point for writing your own. The SDK is MIT licensed and contributions are welcome. If you have questions or ideas, post a question here or open an issue on the repo.Building Interactive Agent UIs with AG-UI and Microsoft Agent Framework
Introduction Picture this: You've built an AI agent that analyzes financial data. A user uploads a quarterly report and asks: "What are the top three expense categories?" Behind the scenes, your agent parses the spreadsheet, aggregates thousands of rows, and generates visualizations. All in 20 seconds. But the user? They see a loading spinner. Nothing else. No "reading file" message, no "analyzing data" indicator, no hint that progress is being made. They start wondering: Is it frozen? Should I refresh? The problem isn't the agent's capabilities - it's the communication gap between the agent running on the backend and the user interface. When agents perform multi-step reasoning, call external APIs, or execute complex tool chains, users deserve to see what's happening. They need streaming updates, intermediate results, and transparent progress indicators. Yet most agent frameworks force developers to choose between simple request/response patterns or building custom solutions to stream updates to their UIs. This is where AG-UI comes in. AG-UI is a fairly new event-based protocol that standardizes how agents communicate with user interfaces. Instead of every framework and development team inventing their own streaming solution, AG-UI provides a shared vocabulary of structured events that work consistently across different agent implementations. When an agent starts processing, calls a tool, generates text, or encounters an error, the UI receives explicit, typed events in real time. The beauty of AG-UI is its framework-agnostic design. While this blog post demonstrates integration with Microsoft Agent Framework (MAF), the same AG-UI protocol works with LangGraph, CrewAI, or any other compliant framework. Write your UI code once, and it works with any AG-UI-compliant backend. (Note: MAF supports both Python and .NET - this blog post focuses on the Python implementation.) TL;DR The Problem: Users don't get real-time updates while AI agents work behind the scenes - no progress indicators, no transparency into tool calls, and no insight into what's happening. The Solution: AG-UI is an open, event-based protocol that standardizes real-time communication between AI agents and user interfaces. Instead of each development team and framework inventing custom streaming solutions, AG-UI provides a shared vocabulary of structured events (like TOOL_CALL_START, TEXT_MESSAGE_CONTENT, RUN_FINISHED) that work across any compliant framework. Key Benefits: Framework-agnostic - Write UI code once, works with LangGraph, Microsoft Agent Framework, CrewAI, and more Real-time observability - See exactly what your agent is doing as it happens Server-Sent Events - Built on standard HTTP for universal compatibility Protocol-managed state - No manual conversation history tracking In This Post: You'll learn why AG-UI exists, how it works, and build a complete working application using Microsoft Agent Framework with Python - from server setup to client implementation. What You'll Learn This blog post walks through: Why AG-UI exists - how agent-UI communication has evolved and what problems current approaches couldn't solve How the protocol works - the key design choices that make AG-UI simple, reliable, and framework-agnostic Protocol architecture - the generic components and how AG-UI integrates with agent frameworks Building an AG-UI application - a complete working example using Microsoft Agent Framework with server, client, and step-by-step setup Understanding events - what happens under the hood when your agent runs and how to observe it Thinking in events - how building with AG-UI differs from traditional APIs, and what benefits this brings Making the right choice - when AG-UI is the right fit for your project and when alternatives might be better Estimated reading time: 15 minutes Who this is for: Developers building AI agents who want to provide real-time feedback to users, and teams evaluating standardized approaches to agent-UI communication To appreciate why AG-UI matters, we need to understand the journey that led to its creation. Let's trace how agent-UI communication has evolved through three distinct phases. The Evolution of Agent-UI Communication AI agents have become more capable over time. As they evolved, the way they communicated with user interfaces had to evolve as well. Here's how this evolution unfolded. Phase 1: Simple Request/Response In the early days of AI agent development, the interaction model was straightforward: send a question, wait for an answer, display the result. This synchronous approach mirrored traditional API calls and worked fine for simple scenarios. # Simple, but limiting response = agent.run("What's the weather in Paris?") display(response) # User waits... and waits... Works for: Quick queries that complete in seconds, simple Q&A interactions where immediate feedback and interactivity aren't critical. Breaks down: When agents need to call multiple tools, perform multi-step reasoning, or process complex queries that take 30+ seconds. Users see nothing but a loading spinner, with no insight into what's happening or whether the agent is making progress. This creates a poor user experience and makes it impossible to show intermediate results or allow user intervention. Recognizing these limitations, development teams began experimenting with more sophisticated approaches. Phase 2: Custom Streaming Solutions As agents became more sophisticated, teams recognized the need for incremental feedback and interactivity. Rather than waiting for the complete response, they implemented custom streaming solutions to show partial results as they became available. # Every team invents their own format for chunk in agent.stream("What's the weather?"): display(chunk) # But what about tool calls? Errors? Progress? This was a step forward for building interactive agent UIs, but each team solved the problem differently. Also, different frameworks had incompatible approaches - some streamed only text tokens, others sent structured JSON, and most provided no visibility into critical events like tool calls or errors. The problem: No standardization across frameworks - client code that works with LangGraph won't work with Crew AI, requiring separate implementations for each agent backend Each implementation handles tool calls differently - some send nothing during tool execution, others send unstructured messages Complex state management - clients must track conversation history, manage reconnections, and handle edge cases manually The industry needed a better solution - a common protocol that could work across all frameworks while maintaining the benefits of streaming. Phase 3: Standardized Protocol (AG-UI) AG-UI emerged as a response to the fragmentation problem. Instead of each framework and development team inventing their own streaming solution, AG-UI provides a shared vocabulary of events that work consistently across different agent implementations. # Standardized events everyone understands async for event in agent.run_stream("What's the weather?"): if event.type == "TEXT_MESSAGE_CONTENT": display_text(event.delta) elif event.type == "TOOL_CALL_START": show_tool_indicator(event.tool_name) elif event.type == "TOOL_CALL_RESULT": show_tool_result(event.result) The key difference is structured observability. Rather than guessing what the agent is doing from unstructured text, clients receive explicit events for every stage of execution: when the agent starts, when it generates text, when it calls a tool, when that tool completes, and when the entire run finishes. What's different: A standardized vocabulary of event types, complete observability into agent execution, and framework-agnostic clients that work with any AG-UI-compliant backend. You write your UI code once, and it works whether the backend uses Microsoft Agent Framework, LangGraph, or any other framework that speaks AG-UI. Now that we've seen why AG-UI emerged and what problems it solves, let's examine the specific design decisions that make the protocol work. These choices weren't arbitrary - each one addresses concrete challenges in building reliable, observable agent-UI communication. The Design Decisions Behind AG-UI Why Server-Sent Events (SSE)? Aspect WebSockets SSE (AG-UI) Complexity Bidirectional Unidirectional (simpler) Firewall/Proxy Sometimes blocked Standard HTTP Reconnection Manual implementation Built-in browser support Use case Real-time games, chat Agent responses (one-way) For agent interactions, you typically only need server→client communication, making SSE a simpler choice. SSE solves the transport problem - how events travel from server to client. But once connected, how does the protocol handle conversation state across multiple interactions? Why Protocol-Managed Threads? # Without protocol threads (client manages): conversation_history = [] conversation_history.append({"role": "user", "content": message}) response = agent.complete(conversation_history) conversation_history.append({"role": "assistant", "content": response}) # Complex, error-prone, doesn't work with multiple clients # With AG-UI (protocol manages): thread = agent.get_new_thread() # Server creates and manages thread agent.run_stream(message, thread=thread) # Server maintains context # Simple, reliable, shareable across clients With transport and state management handled, the final piece is the actual messages flowing through the connection. What information should the protocol communicate, and how should it be structured? Why Standardized Event Types? Instead of parsing unstructured text, clients get typed events: RUN_STARTED - Agent begins (start loading UI) TEXT_MESSAGE_CONTENT - Text chunk (stream to user) TOOL_CALL_START - Tool invoked (show "searching...", "calculating...") TOOL_CALL_RESULT - Tool finished (show result, update UI) RUN_FINISHED - Complete (hide loading) This lets UIs react intelligently without custom parsing logic. Now that we understand the protocol's design choices, let's see how these pieces fit together in a complete system. Architecture Overview Here's how the components interact: The communication between these layers relies on a well-defined set of event types. Here are the core events that flow through the SSE connection: Core Event Types AG-UI provides a standardized set of event types to describe what's happening during an agent's execution: RUN_STARTED - agent begins execution TEXT_MESSAGE_START, TEXT_MESSAGE_CONTENT, TEXT_MESSAGE_END - streaming segments of text TOOL_CALL_START, TOOL_CALL_ARGS, TOOL_CALL_END, TOOL_CALL_RESULT - tool execution events RUN_FINISHED - agent has finished execution RUN_ERROR - error information This model lets the UI update as the agent runs, rather than waiting for the final response. The generic architecture above applies to any AG-UI implementation. Now let's see how this translates to Microsoft Agent Framework. AG-UI with Microsoft Agent Framework While AG-UI is framework-agnostic, this blog post demonstrates integration with Microsoft Agent Framework (MAF) using Python. MAF is available in both Python and .NET, giving you flexibility to build AG-UI applications in your preferred language. Understanding how MAF implements the protocol will help you build your own applications or work with other compliant frameworks. Integration Architecture The Microsoft Agent Framework integration involves several specialized layers that handle protocol translation and execution orchestration: Understanding each layer: FastAPI Endpoint - Handles HTTP requests and establishes SSE connections for streaming AgentFrameworkAgent - Protocol wrapper that translates between AG-UI events and Agent Framework operations Orchestrators - Manage execution flow, coordinate tool calling sequences, and handle state transitions ChatAgent - Your agent implementation with instructions, tools, and business logic ChatClient - Interface to the underlying language model (Azure OpenAI, OpenAI, or other providers) The good news? When you call add_agent_framework_fastapi_endpoint, all the middleware layers are configured automatically. You simply provide your ChatAgent, and the integration handles protocol translation, event streaming, and state management behind the scenes. Now that we understand both the protocol architecture and the Microsoft Agent Framework integration, let's build a working application. Hands-On: Building Your First AG-UI Application This section demonstrates how to build an AG-UI server and client using Microsoft Agent Framework and FastAPI. Prerequisites Before building your first AG-UI application, ensure you have: Python 3.10 or later installed Basic understanding of async/await patterns in Python Azure CLI installed and authenticated (az login) Azure OpenAI service endpoint and deployment configured (setup guide) Cognitive Services OpenAI Contributor role for your Azure OpenAI resource You'll also need to install the AG-UI integration package: pip install agent-framework-ag-ui --pre This automatically installs agent-framework-core, fastapi, and uvicorn as dependencies. With your environment configured, let's create the server that will host your agent and expose it via the AG-UI protocol. Building the Server Let's create a FastAPI server that hosts an AI agent and exposes it via AG-UI: # server.py import os from typing import Annotated from dotenv import load_dotenv from fastapi import FastAPI from pydantic import Field from agent_framework import ChatAgent, ai_function from agent_framework.azure import AzureOpenAIChatClient from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint from azure.identity import DefaultAzureCredential # Load environment variables from .env file load_dotenv() # Validate environment configuration openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") model_deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME") if not openai_endpoint: raise RuntimeError("Missing required environment variable: AZURE_OPENAI_ENDPOINT") if not model_deployment: raise RuntimeError("Missing required environment variable: AZURE_OPENAI_DEPLOYMENT_NAME") # Define tools the agent can use @ai_function def get_order_status( order_id: Annotated[str, Field(description="The order ID to look up (e.g., ORD-001)")] ) -> dict: """Look up the status of a customer order. Returns order status, tracking number, and estimated delivery date. """ # Simulated order lookup orders = { "ORD-001": {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"}, "ORD-002": {"status": "processing", "tracking": None, "eta": "Jan 23, 2026"}, "ORD-003": {"status": "delivered", "tracking": "1Z999AA3", "eta": "Delivered Jan 20"}, } return orders.get(order_id, {"status": "not_found", "message": "Order not found"}) # Initialize Azure OpenAI client chat_client = AzureOpenAIChatClient( credential=DefaultAzureCredential(), endpoint=openai_endpoint, deployment_name=model_deployment, ) # Configure the agent with custom instructions and tools agent = ChatAgent( name="CustomerSupportAgent", instructions="""You are a helpful customer support assistant. You have access to a get_order_status tool that can look up order information. IMPORTANT: When a user mentions an order ID (like ORD-001, ORD-002, etc.), you MUST call the get_order_status tool to retrieve the actual order details. Do NOT make up or guess order information. After calling get_order_status, provide the actual results to the user in a friendly format.""", chat_client=chat_client, tools=[get_order_status], ) # Initialize FastAPI application app = FastAPI( title="AG-UI Customer Support Server", description="Interactive AI agent server using AG-UI protocol with tool calling" ) # Mount the AG-UI endpoint add_agent_framework_fastapi_endpoint(app, agent, path="/chat") def main(): """Entry point for the AG-UI server.""" import uvicorn print("Starting AG-UI server on http://localhost:8000") uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info") # Run the application if __name__ == "__main__": main() What's happening here: We define a tool: get_order_status with the AI_function decorator Use Annotated and Field for parameter descriptions to help the agent understand when and how to use the tool We create an Azure OpenAI chat client with credential authentication The ChatAgent is configured with domain-specific instructions and the tools parameter add_agent_framework_fastapi_endpoint automatically handles SSE streaming and tool execution The server exposes the agent at the /chat endpoint Note: This example uses Azure OpenAI, but AG-UI works with any chat model. You can also integrate with Azure AI Foundry's model catalog or use other LLM providers. Tool calling is supported by most modern LLMs including GPT-4, GPT-4o, and Claude models. To run this server: # Set your Azure OpenAI credentials export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o" # Start the server python server.py With your server running and exposing the AG-UI endpoint, the next step is building a client that can connect and consume the event stream. Streaming Results to Clients With the server running, clients can connect and stream events as the agent processes requests. Here's a Python client that demonstrates the streaming capabilities: # client.py import asyncio import os from dotenv import load_dotenv from agent_framework import ChatAgent, FunctionCallContent, FunctionResultContent from agent_framework_ag_ui import AGUIChatClient # Load environment variables from .env file load_dotenv() async def interactive_chat(): """Interactive chat session with streaming responses.""" # Connect to the AG-UI server base_url = os.getenv("AGUI_SERVER_URL", "http://localhost:8000/chat") print(f"Connecting to: {base_url}\n") # Initialize the AG-UI client client = AGUIChatClient(endpoint=base_url) # Create a local agent representation agent = ChatAgent(chat_client=client) # Start a new conversation thread conversation_thread = agent.get_new_thread() print("Chat started! Type 'exit' or 'quit' to end the session.\n") try: while True: # Collect user input user_message = input("You: ") # Handle empty input if not user_message.strip(): print("Please enter a message.\n") continue # Check for exit commands if user_message.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break # Stream the agent's response print("Agent: ", end="", flush=True) # Track tool calls to avoid duplicate prints seen_tools = set() async for update in agent.run_stream(user_message, thread=conversation_thread): # Display text content if update.text: print(update.text, end="", flush=True) # Display tool calls and results for content in update.contents: if isinstance(content, FunctionCallContent): # Only print each tool call once if content.call_id not in seen_tools: seen_tools.add(content.call_id) print(f"\n[Calling tool: {content.name}]", flush=True) elif isinstance(content, FunctionResultContent): # Only print each result once result_id = f"result_{content.call_id}" if result_id not in seen_tools: seen_tools.add(result_id) result_text = content.result if isinstance(content.result, str) else str(content.result) print(f"[Tool result: {result_text}]", flush=True) print("\n") # New line after response completes except KeyboardInterrupt: print("\n\nChat interrupted by user.") except ConnectionError as e: print(f"\nConnection error: {e}") print("Make sure the server is running.") except Exception as e: print(f"\nUnexpected error: {e}") def main(): """Entry point for the AG-UI client.""" asyncio.run(interactive_chat()) if __name__ == "__main__": main() Key features: The client connects to the AG-UI endpoint using AGUIChatClient with the endpoint parameter run_stream() yields updates containing text and content as they arrive Tool calls are detected using FunctionCallContent and displayed with [Calling tool: ...] Tool results are detected using FunctionResultContent and displayed with [Tool result: ...] Deduplication logic (seen_tools set) prevents printing the same tool call multiple times as it streams Thread management maintains conversation context across messages Graceful error handling for connection issues To use the client: # Optional: specify custom server URL export AGUI_SERVER_URL="http://localhost:8000/chat" # Start the interactive chat python client.py Example Session: Connecting to: http://localhost:8000/chat Chat started! Type 'exit' or 'quit' to end the session. You: What's the status of order ORD-001? Agent: [Calling tool: get_order_status] [Tool result: {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"}] Your order ORD-001 has been shipped! - Tracking Number: 1Z999AA1 - Estimated Delivery Date: January 25, 2026 You can use the tracking number to monitor the delivery progress. You: Can you check ORD-002? Agent: [Calling tool: get_order_status] [Tool result: {"status": "processing", "tracking": null, "eta": "Jan 23, 2026"}] Your order ORD-002 is currently being processed. - Status: Processing - Estimated Delivery: January 23, 2026 Your order should ship soon, and you'll receive a tracking number once it's on the way. You: exit Goodbye! The client we just built handles events at a high level, abstracting away the details. But what's actually flowing through that SSE connection? Let's peek under the hood. Event Types You'll See As the server streams back responses, clients receive a series of structured events. If you were to observe the raw SSE stream (e.g., using curl), you'd see events like: curl -N http://localhost:8000/chat \ -H "Content-Type: application/json" \ -H "Accept: text/event-stream" \ -d '{"messages": [{"role": "user", "content": "What'\''s the status of order ORD-001?"}]}' Sample event stream (with tool calling): data: {"type":"RUN_STARTED","threadId":"eb4d9850-14ef-446c-af4b-23037acda9e8","runId":"chatcmpl-xyz"} data: {"type":"TEXT_MESSAGE_START","messageId":"e8648880-a9ff-4178-a17d-4a6d3ec3d39c","role":"assistant"} data: {"type":"TOOL_CALL_START","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","toolCallName":"get_order_status","parentMessageId":"e8648880-a9ff-4178-a17d-4a6d3ec3d39c"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"{\""} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"order"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"_id"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"\":\""} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"ORD"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"-"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"001"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"\"}"} data: {"type":"TOOL_CALL_END","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y"} data: {"type":"TOOL_CALL_RESULT","messageId":"f048cb0a-a049-4a51-9403-a05e4820438a","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","content":"{\"status\": \"shipped\", \"tracking\": \"1Z999AA1\", \"eta\": \"Jan 25, 2026\"}","role":"tool"} data: {"type":"TEXT_MESSAGE_START","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","role":"assistant"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"Your"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" order"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" ORD"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"-"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"001"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" has"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" been"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" shipped"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"!"} ... (additional TEXT_MESSAGE_CONTENT events streaming the response) ... data: {"type":"TEXT_MESSAGE_END","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf"} data: {"type":"RUN_FINISHED","threadId":"eb4d9850-14ef-446c-af4b-23037acda9e8","runId":"chatcmpl-xyz"} Understanding the flow: RUN_STARTED - Agent begins processing the request TEXT_MESSAGE_START - First message starts (will contain tool calls) TOOL_CALL_START - Agent invokes the get_order_status tool Multiple TOOL_CALL_ARGS events - Arguments stream incrementally as JSON chunks ({"order_id":"ORD-001"}) TOOL_CALL_END - Tool invocation structure complete TOOL_CALL_RESULT - Tool execution finished with result data TEXT_MESSAGE_START - Second message starts (the final response) Multiple TEXT_MESSAGE_CONTENT events - Response text streams word-by-word TEXT_MESSAGE_END - Response message complete RUN_FINISHED - Entire run completed successfully This granular event model enables rich UI experiences - showing tool execution indicators ("Searching...", "Calculating..."), displaying intermediate results, and providing complete transparency into the agent's reasoning process. Seeing the raw events helps, but truly working with AG-UI requires a shift in how you think about agent interactions. Let's explore this conceptual change. The Mental Model Shift Traditional API Thinking # Imperative: Call and wait response = agent.run("What's 2+2?") print(response) # "The answer is 4" Mental model: Function call with return value AG-UI Thinking # Reactive: Subscribe to events async for event in agent.run_stream("What's 2+2?"): match event.type: case "RUN_STARTED": show_loading() case "TEXT_MESSAGE_CONTENT": display_chunk(event.delta) case "RUN_FINISHED": hide_loading() Mental model: Observable stream of events This shift feels similar to: Moving from synchronous to async code Moving from REST to event-driven architecture Moving from polling to pub/sub This mental shift isn't just philosophical - it unlocks concrete benefits that weren't possible with request/response patterns. What You Gain Observability # You can SEE what the agent is doing TOOL_CALL_START: "get_order_status" TOOL_CALL_ARGS: {"order_id": "ORD-001"} TOOL_CALL_RESULT: {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"} TEXT_MESSAGE_START: "Your order ORD-001 has been shipped..." Interruptibility # Future: Cancel long-running operations async for event in agent.run_stream(query): if user_clicked_cancel: await agent.cancel(thread_id, run_id) break Transparency # Users see the reasoning process "Looking up order ORD-001..." "Order found: Status is 'shipped'" "Retrieving tracking information..." "Your order has been shipped with tracking number 1Z999AA1..." To put these benefits in context, here's how AG-UI compares to traditional approaches across key dimensions: AG-UI vs. Traditional Approaches Aspect Traditional REST Custom Streaming AG-UI Connection Model Request/Response Varies Server-Sent Events State Management Manual Manual Protocol-managed Tool Calling Invisible Custom format Standardized events Framework Varies Framework-locked Framework-agnostic Browser Support Universal Varies Universal Implementation Simple Complex Moderate Ecosystem N/A Isolated Growing You've now seen AG-UI's design principles, implementation details, and conceptual foundations. But the most important question remains: should you actually use it? Conclusion: Is AG-UI Right for Your Project? AG-UI represents a shift toward standardized, observable agent interactions. Before adopting it, understand where the protocol stands and whether it fits your needs. Protocol Maturity The protocol is stable enough for production use but still evolving: Ready now: Core specification stable, Microsoft Agent Framework integration available, FastAPI/Python implementation mature, basic streaming and threading work reliably. Choose AG-UI If You Building new agent projects - No legacy API to maintain, want future compatibility with emerging ecosystem Need streaming observability - Multi-step workflows where users benefit from seeing each stage of execution Want framework flexibility - Same client code works with any AG-UI-compliant backend Comfortable with evolving standards - Can adapt to protocol changes as it matures Stick with Alternatives If You Have working solutions - Custom streaming working well, migration cost not justified Need guaranteed stability - Mission-critical systems where breaking changes are unacceptable Build simple agents - Single-step request/response without tool calling or streaming needs Risk-averse environment - Large existing implementations where proven approaches are required Beyond individual project decisions, it's worth considering AG-UI's role in the broader ecosystem. The Bigger Picture While this blog post focused on Microsoft Agent Framework, AG-UI's true power lies in its broader mission: creating a common language for agent-UI communication across the entire ecosystem. As more frameworks adopt it, the real value emerges: write your UI once, work with any compliant agent framework. Think of it like GraphQL for APIs or OpenAPI for REST - a standardization layer that benefits the entire ecosystem. The protocol is young, but the problem it solves is real. Whether you adopt it now or wait for broader adoption, understanding AG-UI helps you make informed architectural decisions for your agent applications. Ready to dive deeper? Here are the official resources to continue your AG-UI journey. Resources AG-UI & Microsoft Agent Framework Getting Started with AG-UI (Microsoft Learn) - Official tutorial AG-UI Integration Overview - Architecture and concepts AG-UI Protocol Specification - Official protocol documentation Backend Tool Rendering - Adding function tools Security Considerations - Production security guidance Microsoft Agent Framework Documentation - Framework overview AG-UI Dojo Examples - Live demonstrations UI Components & Integration CopilotKit for Microsoft Agent Framework - React component library Community & Support Microsoft Q&A - Community support Agent Framework GitHub - Source code and issues Related Technologies Azure AI Foundry Documentation - Azure AI platform FastAPI Documentation - Web framework Server-Sent Events (SSE) Specification - Protocol standard This blog post introduces AG-UI with Microsoft Agent Framework, focusing on fundamental concepts and building your first interactive agent application.How to Build Safe Natural Language-Driven APIs
TL;DR Building production natural language APIs requires separating semantic parsing from execution. Use LLMs to translate user text into canonical structured requests (via schemas), then execute those requests deterministically. Key patterns: schema completion for clarification, confidence gates to prevent silent failures, code-based ontologies for normalization, and an orchestration layer. This keeps language as input, not as your API contract. Introduction APIs that accept natural language as input are quickly becoming the norm in the age of agentic AI apps and LLMs. From search and recommendations to workflows and automation, users increasingly expect to "just ask" and get results. But treating natural language as an API contract introduces serious risks in production systems: Nondeterministic behavior Prompt-driven business logic Difficult debugging and replay Silent failures that are hard to detect In this post, I'll describe a production-grade architecture for building safe, natural language-driven APIs: one that embraces LLMs for intent discovery and entity extraction while preserving the determinism, observability, and reliability that backend systems require. This approach is based on building real systems using Azure OpenAI and LangGraph, and on lessons learned the hard way. The Core Problem with Natural Language APIs Natural language is an excellent interface for humans. It is a poor interface for systems. When APIs accept raw text directly and execute logic based on it, several problems emerge: The API contract becomes implicit and unversioned Small prompt changes cause behavioral changes Business logic quietly migrates into prompts In short: language becomes the contract, and that's fragile. The solution is not to avoid natural language, but to contain it. A Key Principle: Natural Language Is Input, Not a Contract So how do we contain it? The answer lies in treating natural language fundamentally differently than we treat traditional API inputs. The most important design decision we made was this: Natural language should be translated into structure, not executed directly. That single principle drives the entire architecture. Instead of building "chatty APIs," we split responsibilities clearly: Natural language is used for intent discovery and entity extraction Structured data is used for execution Two Explicit API Layers This principle translates into a concrete architecture with two distinct API layers, each with a single, clear responsibility. 1. Semantic Parse API (Natural Language → Structure) This API: Accepts user text Extracts intent and entities using LLMs Completes a predefined schema Asks clarifying questions when required Returns a canonical, structured request Does not execute business logic Think of this as a compiler, not an engine. 2. Structured Execution API (Structure → Action) This API: Accepts only structured input Calls downstream systems to process the request and get results Is deterministic and versioned Contains no natural language handling Is fully testable and replayable This is where execution happens. Why This Separation Matters Separating these layers gives you: A stable, versionable API contract Freedom to improve NLP without breaking clients Clear ownership boundaries Deterministic execution paths Most importantly, it prevents LLM behavior from leaking into core business logic. Canonical Schemas Are the Backbone Now that we've established the two-layer architecture, let's dive into what makes it work: canonical schemas. Each supported intent is defined by a canonical schema that lives in code. Example (simplified): This schema is used when a user is looking for similar product recommendations. The entities capture which product to use as reference and how to bias the recommendations toward price or quality. { "intent": "recommend_similar", "entities": { "reference_product_id": "string", "price_bias": "number (-1 to 1)", "quality_bias": "number (-1 to 1)" } } Schemas define: Required vs optional fields Allowed ranges and types Validation rules They are the contract, not the prompt. When a user says "show me products like the blue backpack but cheaper", the LLM extracts: Intent: recommend_similar reference_product_id: "blue_backpack_123" price_bias: -0.8 (strongly prefer cheaper) quality_bias: 0.0 (neutral) The schema ensures that even if the user phrased it as "find alternatives to item 123 with better pricing" or "cheaper versions of that blue bag", the output is always the same structure. The natural language variation is absorbed at the semantic layer. The execution layer receives a consistent, validated request every time. This decoupling is what makes the system maintainable. Schema Completion, Not Free-Form Chat But what happens when the user's input doesn't contain all the information needed to complete the schema? This is where structured clarification comes in. A common misconception is that clarification means "chatting until it feels right." In production systems, clarification is schema completion. If required fields are missing or ambiguous, the semantic API responds with: What information is missing A targeted clarification question The current schema state Example response: { "status": "needs_clarification", "missing_fields": ["reference_product_id"], "question": "Which product should I compare against?", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } The state object is the memory. The API itself remains stateless. A Complete Conversation Flow To illustrate how schema completion works in practice, here's a full conversation flow where the user's initial request is missing required information: Initial Request: User: "Show me cheaper alternatives with good quality" API Response (needs clarification): { "status": "needs_clarification", "missing_fields": ["reference_product_id"], "question": "Which product should I compare against?", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } Follow-up Request: User: "The blue backpack" Client sends: { "user_input": "The blue backpack", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } API Response (complete): { "status": "complete", "canonical_request": { "intent": "recommend_similar", "entities": { "reference_product_id": "blue_backpack_123", "price_bias": -0.3, "quality_bias": 0.4 } } } The client passes the state back with each clarification. The API remains stateless, while the client manages the conversation context. Once complete, the canonical_request can be sent directly to the execution API. Why LangGraph Fits This Problem Perfectly With schemas and clarification flows defined, we need a way to orchestrate the semantic parsing workflow reliably. This is where LangGraph becomes valuable. LangGraph allows semantic parsing to be modeled as a structured, deterministic workflow with explicit decision points: Classify intent: Determine what the user wants to do from a predefined set of supported actions Extract candidate entities: Pull out relevant parameters from the natural language input using the LLM Merge into schema state: Map the extracted values into the canonical schema structure Validate required fields: Check if all mandatory fields are present and values are within acceptable ranges Either complete or request clarification: Return the canonical request if complete, or ask a targeted question if information is missing Each node has a single responsibility. Validation and routing are done in code, not by the LLM. LangGraph provides: Explicit state transitions Deterministic routing Observable execution Safe retries Used this way, it becomes a powerful orchestration tool, not a conversational agent. Confidence Gates Prevent Silent Failures Structured workflows handle the process, but there's another critical safety mechanism we need: knowing when the LLM isn't confident about its extraction. Even when outputs are structurally valid, they may not be reliable. We require the semantic layer to emit a confidence score. If confidence falls below a threshold, execution is blocked and clarification is requested. This simple rule eliminates an entire class of silent misinterpretations that are otherwise very hard to detect. Example: When a user says "Show me items similar to the bag", the LLM might extract: { "intent": "recommend_similar", "confidence": 0.55, "entities": { "reference_product_id": "generic_bag_001", "confidence_scores": { "reference_product_id": 0.4 } } } The overall confidence is low (0.55), and the entity confidence for reference_product_id is very low (0.4) because "the bag" is ambiguous. There might be hundreds of bags in the catalog. Instead of proceeding with a potentially wrong guess, the API responds: { "status": "needs_clarification", "reason": "low_confidence", "question": "I found multiple bags. Did you mean the blue backpack, the leather tote, or the travel duffel?", "confidence": 0.55 } This prevents the system from silently executing the wrong recommendation and provides a better user experience. Lightweight Ontologies (Keep Them in Code) Beyond confidence scoring, we need a way to normalize the variety of terms users might use into consistent canonical values. We also introduced lightweight, code-level ontologies: Allowed intents Required entities per intent Synonym-to-canonical mappings Cross-field validation rules These live in code and configuration, not in prompts. LLMs propose values. Code enforces meaning. Example: Consider these user inputs that all mean the same thing: "Show me cheaper options" "Find budget-friendly alternatives" "I want something more affordable" "Give me lower-priced items" The LLM might extract different values: "cheaper", "budget-friendly", "affordable", "lower-priced". The ontology maps all of these to a canonical value: PRICE_BIAS_SYNONYMS = { "cheaper": -0.7, "budget-friendly": -0.7, "affordable": -0.7, "lower-priced": -0.7, "expensive": 0.7, "premium": 0.7, "high-end": 0.7 } When the LLM extracts "budget-friendly", the code normalizes it to -0.7 for the price_bias field. Similarly, cross-field validation catches logical inconsistencies: if entities["price_bias"] < -0.5 and entities["quality_bias"] > 0.5: return clarification("You want cheaper items with higher quality. This might be difficult. Should I prioritize price or quality?") The LLM proposes. The ontology normalizes. The validation enforces business rules. What About Latency? A common concern with multi-step semantic parsing is performance. In practice, we observed: Intent classification: ~40 ms Entity extraction: ~200 ms Validation and routing: ~1 ms Total overhead: ~250–300 ms. For chat-driven user experiences, this is well within acceptable bounds and far cheaper than incorrect or inconsistent execution. Key Takeaways Let's bring it all together. If you're building APIs that accept natural language in production: Do not make language your API contract Translate language into canonical structure Own schema completion server-side Use LLMs for discovery and extraction, not execution Treat safety and determinism as first-class requirements Natural language is an input format. Structure is the contract. Closing Thoughts LLMs make it easy to build impressive demos. Building safe, reliable systems with them requires discipline. By separating semantic interpretation from execution, and by using tools like Azure OpenAI and LangGraph thoughtfully, you can build natural language-driven APIs that scale, evolve, and behave predictably in production. Hopefully, this architecture saves you a few painful iterations.Using on-behalf-of flow for Entra-based MCP servers
In December, we presented a series about MCP, culminating in a session about adding authentication to MCP servers. I demoed a Python MCP server that uses Microsoft Entra for authentication, requiring users to first login to the Microsoft tenant before they could use a tool. Many developers asked how they could take the Entra integration further, like to check the user's group membership or query their OneDrive. That requires using an "on-behalf-of" flow, where the MCP server uses the user's Entra identity to call another API, like the Microsoft Graph API. In this blog post, I will explain how to use Entra with OBO flow in a Python FastMCP server. How MCP servers can use Entra authentication The MCP authorization specification is based on OAuth2, with some additional features tacked on top. Every MCP client is actually an OAuth2 client, and each MCP server is an OAuth2 resource server: MCP auth adds these features to help clients determine how to authorize a server: Protected resource metadata (PRM): Implemented on the MCP server, provides details about the authorization server and method Authorization server metadata: Implemented on the authorization server, gives URLs for OAuth2 endpoints Additionally, to allow MCP servers to work with arbitrary MCP clients, MCP auth supports either of these client registration methods: Dynamic Client Registration (DCR): Implemented on the authorization server, it can register new MCP clients as OAuth2 clients, even if it hasn't seen them before. Client ID Metadata Documents (CIMD): An alternative to DCR, this requires both the MCP client to make a CIMD document available on a server, and requires the authorization server to fetch the CIMD document for details about the client. Microsoft Entra does support authorization server metadata, but it does not support either DCR or CIMD. That's actually fine if you are building an MCP server that's only going to be used with pre-authorized clients, like if the server will only be used with VS Code or with a specific internal MCP client. But, if you are building an MCP server that can be used with arbitrary MCP clients, then either DCR or CIMD is required. So what do we do? Fortunately, the FastMCP SDK implements DCR on top of Entra using an OAuth proxy pattern. FastMCP acts as the authorization server, intercepting requests and forwarding to Entra when needed, and storing OAuth client information in a designated database (like in-memory or Cosmos DB). ⚠️ Warning: This proxy approach is intended only for development and testing scenarios. For production deployments, Microsoft recommends using pre‑registered client applications where client identifiers and permissions are explicitly created, reviewed, and approved on a per-app basis. Let's walk through the steps to set that up. Registering the server with Entra Before the server can use Entra to authorize users, we need to register the server with Entra via an app registration. We can do registration using the Azure Portal, Azure CLI, Microsoft Graph SDK, or even Bicep. In this demo, I use the Python MS Graph SDK as it allows me to specify everything programmatically. First, I create the Entra app registration, specifying the sign-in audience (single-tenant), redirect URIs (including local MCP server, deployed MCP server, and VS Code redirect URIs), and the scopes for the exposed API. request_app = Application( display_name="FastMCP Server App", sign_in_audience="AzureADMyOrg", # Single tenant web=WebApplication( redirect_uris=[ "http://localhost:8000/auth/callback", "https://vscode.dev/redirect", "http://127.0.0.1:33418", "https://deployedurl.com/auth/callback" ], ), api=ApiApplication( oauth2_permission_scopes=[ PermissionScope( id=uuid.UUID("{" + str(uuid.uuid4()) + "}"), admin_consent_display_name="Access FastMCP Server", admin_consent_description="Allows access to the FastMCP server as the signed-in user.", user_consent_display_name="Access FastMCP Server", user_consent_description="Allow access to the FastMCP server on your behalf", is_enabled=True, value="mcp-access", type="User", )], requested_access_token_version=2, # Required by FastMCP ) ) app = await graph_client.applications.post(request_app) await graph_client.applications.by_application_id(app.id).patch( Application(identifier_uris=[f"api://{app.app_id}"])) Thanks to that configuration, when an MCP client like VS Code requests an OAuth2 token, it will request a token with the scope "api://{app.app_id}/mcp-access", and the FastMCP server will validate that incoming tokens contain that scope. Next, I create a Service Principal for that Entra app registration, which represents the Entra app in my tenant request_principal = ServicePrincipal(app_id=app.app_id, display_name=app.display_name) await graph_client.service_principals.post(request_principal) I need a way for the server to prove that it can use that Entra app registration, so I register a secret: password_credential = await graph_client.applications.by_application_id(app.id).add_password.post( AddPasswordPostRequestBody( password_credential=PasswordCredential(display_name="FastMCPSecret"))) Ideally, I would like to move away from secrets, as Entra now has support for using federated identity credentials for Entra app registrations instead, but that form of credential isn't supported yet in the FastMCP SDK. If you choose to use a secret, make sure that you store the secret securely. Granting admin consent This next step is only necessary when our MCP server wants to use an OBO flow to exchange access tokens for other resource server tokens (Graph API tokens, in this case). For the OBO flow to work, the Entra app registration needs permission to call the Graph API on behalf of users. If we controlled the client, we could force it to request the required scopes as part of the initial login dialog. However, since we are configuring this server to work with arbitrary MCP clients, we don't have that option. Instead, we grant admin consent to the Entra app for the necessary scopes, such that no Graph API consent dialog is needed. This code grants admin consent to the associated service principal for the Graph API resource and scopes: server_principal = await graph_client.service_principals_with_app_id(app.app_id).get() grant = GrantDefinition( principal_id=server_principal.id, resource_app_id="00000003-0000-0000-c000-000000000000", # Graph API scopes=["User.Read", "email", "offline_access", "openid", "profile"], target_label="server application") resource_principal = await graph_client.service_principals_with_app_id(grant.resource_app_id).get() desired_scope = grant.scope_string() await graph_client.oauth2_permission_grants.post( OAuth2PermissionGrant( client_id=grant.principal_id, consent_type="AllPrincipals", resource_id=resource_principal.id, scope=desired_scope)) If our MCP server needed to use an OBO flow with another resource server, we could request additional grants for those resources and scopes. Our Entra app registration is now ready for the MCP server, so let's move on to see the server code. Using FastMCP servers with Entra In our MCP server code, we configure FastMCP's built in AzureProvider based off the details from the Entra app registration process: auth = AzureProvider( client_id=os.environ["ENTRA_PROXY_AZURE_CLIENT_ID"], client_secret=os.environ["ENTRA_PROXY_AZURE_CLIENT_SECRET"], tenant_id=os.environ["AZURE_TENANT_ID"], base_url=entra_base_url, # MCP server URL required_scopes=["mcp-access"], client_storage=oauth_client_store, # in-memory or Cosmos DB ) To make it easy for our MCP tools to access an identifier for the currently logged in user, we define a middleware that inspects the claims of the current token using FastMCP's get_access_token() and sets the "oid" (Entra object identifier) in the state: class UserAuthMiddleware(Middleware): def _get_user_id(self): token = get_access_token() if not (token and hasattr(token, "claims")): return None return token.claims.get("oid") async def on_call_tool(self, context: MiddlewareContext, call_next): user_id = self._get_user_id() if context.fastmcp_context is not None: context.fastmcp_context.set_state("user_id", user_id) return await call_next(context) async def on_read_resource(self, context: MiddlewareContext, call_next): user_id = self._get_user_id() if context.fastmcp_context is not None: context.fastmcp_context.set_state("user_id", user_id) return await call_next(context) When we initialize the FastMCP server, we set the auth provider and include that middleware: mcp = FastMCP("Expenses Tracker", auth=auth, middleware=[UserAuthMiddleware()]) Now, every request made to the MCP server will require authentication. The server will return a 401 if a valid token isn't provided, and that 401 will prompt the MCP client to kick off the MCP authorization flow. Inside each tool, we can grab the user id from the state, and use that to customize the response for the user, like to store or query items in a database. .tool async def add_user_expense( date: Annotated[date, "Date of the expense in YYYY-MM-DD format"], amount: Annotated[float, "Positive numeric amount of the expense"], description: Annotated[str, "Human-readable description of the expense"], ctx: Context, ): """Add a new expense to Cosmos DB.""" user_id = ctx.get_state("user_id") if not user_id: return "Error: Authentication required (no user_id present)" expense_item = { "id": str(uuid.uuid4()), "user_id": user_id, "date": date.isoformat(), "amount": amount, "description": description } await cosmos_container.create_item(body=expense_item) Using OBO flow in FastMCP server Now we can move on to using an OBO flow inside an MCP tool, to access the Graph API on behalf of the user. To make it easy to exchange Entra tokens for Graph tokens, we use the Python MSAL SDK, configuring a ConfidentialClientApplication based on our Entra app registration details: confidential_client = ConfidentialClientApplication( client_id=os.environ["ENTRA_PROXY_AZURE_CLIENT_ID"], client_credential=os.environ["ENTRA_PROXY_AZURE_CLIENT_SECRET"], authority=f"https://login.microsoftonline.com/{os.environ['AZURE_TENANT_ID']}", token_cache=TokenCache(), ) Inside the tool that requires OBO, we ask MSAL to exchange the MCP access token for a Graph API access token: access_token = get_access_token() graph_resource_access_token = confidential_client.acquire_token_on_behalf_of( user_assertion=access_token.token, scopes=["https://graph.microsoft.com/.default"] ) graph_token = graph_resource_access_token["access_token"] Once we successfully acquire the token, we can use that token with the Graph API, for any operations permitted by the scopes in the admin consent granted earlier. For this example, we call the Graph API to check whether the logged in user is a member of a particular Entra group, and restrict tool usage if not: async with httpx.AsyncClient() as client: url = ("https://graph.microsoft.com/v1.0/me/transitiveMemberOf/microsoft.graph.group" f"?$filter=id eq '{group_id}'&$count=true") response = await client.get( url, headers={ "Authorization": f"Bearer {graph_token}", "ConsistencyLevel": "eventual", }) data = response.json() membership_count = data.get("@odata.count", 0) You could imagine many other ways to use an OBO flow however, like to query for more details from the Graph API, upload documents to OneDrive/SharePoint/Notes, send emails, and more! All together now For the full code, check out the open source python-mcp-demos repository, and follow the deployment steps for Entra. The most relevant code files are: auth_init.py: Creates the Entra app registration, service principal, client secret, and grants admin consent for OBO flow. auth_update.py: Updates the app registration's redirect URIs after deployment, adding the deployed server URL. auth_entra_mcp.py: The MCP server itself, configured with FastMCP's AzureProvider and tools that use OBO for group membership checks. I want to reiterate once more that the OAuth proxy approach is intended only for development and testing scenarios. For production deployments, Microsoft recommends using pre‑registered client applications where client identifiers and permissions are explicitly created, reviewed, and approved on a per-app basis. I hope that in the future, Entra will formally support MCP authorization via the CIMD protocol, so that we can build MCP servers with Entra auth that work with MCP clients in a fully secure and production-ready way. As always, please let us know if you have further questions or ideas for other Entra integrations. Acknowledgements: Thank you to Matt Gotteiner for his guidance in implementing the OBO flow and review of the blog post.Join our free livestream series on building agents in Python
Join us for a new 6‑part livestream series where we explore the foundational concepts behind building AI agents in Python using the Microsoft Agent Framework. This series is for anyone who wants to understand how agents work—how they call tools, use memory and context, and construct workflows on top of them. Over two weeks, we’ll dive into the practical building blocks that shape real agent behavior. You’ll learn how to: 🔧 Register and structure tools 🔗 Connect local MCP servers 📚 Add context with database calls 🧠 Add memory for personalization 📈 Monitor agent behavior with OpenTelemetry ✅ Evaluate the quality of agent output Throughout the series, we’ll use Python for all live examples and share full code so you can run everything yourself. You can also follow along live using GitHub Models and GitHub Codespaces. 👉 Register for the full series. Spanish speaker? ¡Tendremos una serie para hispanohablantes! Regístrese aquí In addition to the live streams, you can also join Join the Microsoft Foundry Discord to ask follow-up questions after each stream. If you are brand new to generative AI with Python, start with our 9-part Python + AI series, which covers topics such as LLMs, embedding models, RAG, tool calling, MCP, and will prepare you perfectly for the agents series. To learn more about each live stream or register for individual sessions, scroll down: Python + Agents: Building your first agent in Python 24 February, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In the first session of our Python + Agents series, we’ll kick things off with the fundamentals: what AI agents are, how they work, and how to build your first one using the Microsoft Agent Framework. We’ll start with the core anatomy of an agent, then walk through how tool calling works in practice—beginning with a single tool, expanding to multiple tools, and finally connecting to tools exposed through local MCP servers. We’ll conclude with the supervisor agent pattern, where a single supervisor agent coordinates subtasks across multiple subagents, by treating each agent as a tool. Along the way, we'll share tips for debugging and inspecting agents, like using the DevUI interface from Microsoft Agent Framework for interacting with agent prototypes. Python + Agents: Adding context and memory to agents 25 February, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In the second session of our Python + Agents series, we’ll extend agents built with the Microsoft Agent Framework by adding two essential capabilities: context and memory. We’ll begin with context, commonly known as Retrieval‑Augmented Generation (RAG), and show how agents can ground their responses using knowledge retrieved from local data sources such as SQLite or PostgreSQL. This enables agents to provide accurate, domain‑specific answers based on real information rather than model hallucination. Next, we’ll explore memory—both short‑term, thread‑level context and long‑term, persistent memory. You’ll see how agents can store and recall information using solutions like Redis or open‑source libraries such as Mem0, enabling them to remember previous interactions, user preferences, and evolving tasks across sessions. By the end, you’ll understand how to build agents that are not only capable but context‑aware and memory‑efficient, resulting in richer, more personalized user experiences. Python + Agents: Monitoring and evaluating agents 26 February, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In the third session of our Python + Agents series, we’ll focus on two essential components of building reliable agents: observability and evaluation. We’ll begin with observability, using OpenTelemetry to capture traces, metrics, and logs from agent actions. You'll learn how to instrument your agents and use a local Aspire dashboard to identify slowdowns and failures. From there, we’ll explore how to evaluate agent behavior using the Azure AI Evaluation SDK. You’ll see how to define evaluation criteria, run automated assessments over a set of tasks, and analyze the results to measure accuracy, helpfulness, and task success. By the end of the session, you’ll have practical tools and workflows for monitoring, measuring, and improving your agents—so they’re not just functional, but dependable and verifiably effective. Python + Agents: Building your first AI-driven workflows 3 March, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In Session 4 of our Python + Agents series, we’ll explore the foundations of building AI‑driven workflows using the Microsoft Agent Framework: defining workflow steps, connecting them, passing data between them, and introducing simple ways to guide the path a workflow takes. We’ll begin with a conceptual overview of workflows and walk through their core components: executors, edges, and events. You’ll learn how workflows can be composed of simple Python functions or powered by full AI agents when a step requires model‑driven behavior. From there, we’ll dig into conditional branching, showing how workflows can follow different paths depending on model outputs, intermediate results, or lightweight decision functions. We’ll introduce structured outputs as a way to make branching more reliable and easier to maintain—avoiding vague string checks and ensuring that workflow decisions are based on clear, typed data. We'll discover how the DevUI interface makes it easier to develop workflows by visualizing the workflow graph and surfacing the streaming events during a workflow's execution. Finally, we'll dive into an E2E demo application that uses workflows inside a user-facing application with a frontend and backend. Python + Agents: Orchestrating advanced multi-agent workflows 4 March, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In Session 5 of our Python + Agents series, we’ll go beyond workflow fundamentals and explore how to orchestrate advanced, multi‑agent workflows using the Microsoft Agent Framework. This session focuses on patterns that coordinate multiple steps or multiple agents at once, enabling more powerful and flexible AI‑driven systems. We’ll begin by comparing sequential vs. concurrent execution, then dive into techniques for running workflow steps in parallel. You’ll learn how fan‑out and fan‑in edges enable multiple branches to run at the same time, how to aggregate their results, and how concurrency allows workflows to scale across tasks efficiently. From there, we’ll introduce two multi‑agent orchestration approaches that are built into the framework. We’ll start with handoff, where control moves entirely from one agent to another based on workflow logic, which is useful for routing tasks to the right agent as the workflow progresses. We’ll then look at Magentic, a planning‑oriented supervisor that generates a high‑level plan for completing a task and delegates portions of that plan to other agents. Finally, we'll wrap up with a demo of an E2E application that showcases a concurrent multi-agent workflow in action. Python + Agents: Adding a human in the loop to agentic workflows 5 March, 2026 | 6:30 PM - 7:30 PM (UTC) Coordinated Universal Time Register for the stream on Reactor In the final session of our Python + Agents series, we’ll explore how to incorporate human‑in‑the‑loop (HITL) interactions into agentic workflows using the Microsoft Agent Framework. This session focuses on adding points where a workflow can pause, request input or approval from a user, and then resume once the human has responded. HITL is especially important because LLMs can produce uncertain or inconsistent outputs, and human checkpoints provide an added layer of accuracy and oversight. We’ll begin with the framework’s requests‑and‑responses model, which provides a structured way for workflows to ask questions, collect human input, and continue execution with that data. We'll move onto tool approval, one of the most frequent reasons an agent requests input from a human, and see how workflows can surface pending tool calls for approval or rejection. Next, we’ll cover checkpoints and resuming, which allow workflows to pause and be restarted later. This is especially important for HITL scenarios where the human may not be available immediately. We’ll walk through examples that demonstrate how checkpoints store progress, how resuming picks up the workflow state, and how this mechanism supports longer‑running or multi‑step review cycles. This session brings together everything from the series—agents, workflows, branching, orchestration—and shows how to integrate humans thoughtfully into AI‑driven processes, especially when reliability and judgment matter most.Data Security: Azure key Vault in Data bricks
Why this article? To remove the vulnerability of exposing the data base connection string in Databricks notebook directly, by using Azure key vault. Database connection strings are extremely confidential/vulnerable data, that we should not be exposed in the DataBricks notebook explicitly. Azure key vault is a secure option to read the secrets and establish connection. What do we need? Tenant Id of the app from the app registration with access to the azure key vault secrets Client Id of the of the app from the app registration with access to the azure key vault secrets Client secret of the app from the app registration with access to the azure key vault Where to find this information? Under the App registration, you can find the (application) Client Id, Directory (tenant) Id. Client secret value is found in the app registration of the service, under Manage -> Certificate & secrets. You can use an existing secret or create a new one and use it to access the key Vault secrets. Make sure the application is added with get access to read the secrets. Verify the key vault you are checking and using in Databricks is the same one with read access. You can verify this by going to the Azure key vault -> Access Policies and search for the application name. It should show up on search as below, this will confirm that the access of the application. What do we need to setup in Databricks notebook? Open your cluster and install azure.keyvault and azure-identity (installing version should be compatible with you cluster configuration, refer: https://docs.databricks.com/aws/en/libraries/package-repositories) In a new notebook, let’s start by importing the necessary modules. Your notebook would start with the modules, followed by tentatId, clientId, client secret, azure key vault URL , secretName of the connection string in the azure key vault and secretVersion. Lastly, we need to fetch the secret using the below code Vola, we have the DB connection string to perform the CRUD operations. Conclusion: By securely retrieving your database connection string from Azure Key Vault, you eliminate credential exposure and strengthen the overall security posture of your Databricks workflows. This simple shift ensures your notebooks remain clean, compliant, and production‑ready.
