# Frontier Agents Workshop > Microsoft Agent Framework - learn how to adress agentic enterprise scenarios - **Type:** MCP server - **Install:** `agentstack add mcp-denniszielke-frontier-agents-workshop` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [denniszielke](https://agentstack.voostack.com/s/denniszielke) - **Installs:** 0 - **Category:** [Integrations](https://agentstack.voostack.com/c/integrations) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [denniszielke](https://github.com/denniszielke) - **Source:** https://github.com/denniszielke/frontier-agents-workshop ## Install ```sh agentstack add mcp-denniszielke-frontier-agents-workshop ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Microsoft Agent Framework - learn how to adress agentic enterprise scenarios Join us for an immersive hands-on lab focused on Microsoft Agent Framework, where you will learn how to build intelligent multi-agent systems that leverage Azure AI capabilities. This session is designed for architects, developers, and AI enthusiasts who want to explore practical implementations of orchestration patterns. ## Table of Contents 1. [Overview](#overview) 2. [Prerequisites](#prerequisites) 3. [Learning Goals](#learning-goals-for-this-workshop) 3. [Environment Setup](#environment-setup) 4. [Workshop Scenarios](#workshop-scenarios) 5. [Troubleshooting](#troubleshooting) 6. [Additional Resources](#additional-resources) ## What is an Agent? > ***agent***: perceives its environment, makes decisions, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge ## Learning Goals for this workshop This workshop is about learning to work with the Microsoft Agent Framwork - independently of AI Foundry. ### 🛠 Agent Authoring Learning to use the *Microsoft Agent Framework* SDK for code-based and declarative agent development. ### 🚀 Agent Debugging Build, Test and Deploy agents using the DevUI, Foundry UI or VSCode Extension. ### 🔌 Connecting Agents Connect agents using AG-UI, MCP, A2A Protocols to integrate them into your solutions. ### ⚙️ Multi-Agent Orchestration Define multi-agent orchestration workflows to solve complex problems with agents. ### 🔍 Agent Tracing & Evaluation Trace agent behaviour, execution flows and evaluate quality and safety of agents. ## Prerequisites - Python 3.10 or later - An GitHub account with a developer access token - Optional: Redis, AI Search and Application Insights ## Environment Setup 1. **Install packages** (will be automatically done when using Codespaces) ```bash pip install -r requirements.txt ``` 2a. **Configure environment variables (for GitHub Models)** This project supports GitHub AI models via a personal access token (PAT). 1. Create a personal access token To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. [GitHub Free AI Token](https://github.com/settings/tokens) You can now access AI inference with your GitHub PAT. [Learn more about limits based on your plan](https://github.com/marketplace/models/azure-openai/gpt-4o-mini/playground#:~:text=Learn%20more%20about%20limits%20based%20on%20your%20plan.). You do not need to give any permissions to the token besides Model usage. 2. Configure the `.env` file Rename the file `.env.example` to `.env` and set the `GITHUB_TOKEN` value: ```bash GITHUB_TOKEN="" ``` Each Python script will load the values from the `.env` file automatically. 2b. **Configure environment variables (for Azure OpenAI Models)** If you want to use Azure OpenAI instead of (or in addition to) GitHub Models, also set the Azure-specific values in your `.env` file: ```bash AZURE_OPENAI_API_KEY="" AZURE_OPENAI_ENDPOINT="https://.openai.azure.com/openai/v1/" AZURE_OPENAI_VERSION="2024-10-01-preview" COMPLETION_DEPLOYMENT_NAME="" SMALL_DEPLOYMENT_MODEL_NAME="" RESPONSES_DEPLOYMENT_NAME="" ``` The default values and structure for these settings are documented in the `.env.example` file. ## Workshop Scenarios This workshop is organized into seven independent, progressively more advanced scenarios. Each scenario has its own `README.md` in `src/scenarios` describing goals, tasks, references, and example prompts. You do not have to use **[Spec Kit](https://speckit.org/)** for this workshop but we recommend to take a look because it a great way to practice professional software engineering with AI assistance. 1. **Scenario 1 – learning how to build your first agent** Learn how to define a basic agent, connect tools, and use the Agent Framework Dev UI to inspect activities, metrics, and traces while the agent answers time and weather questions and maintains conversational memory. See [Scenario 1 README](src/scenarios/01-hello-world-agent/README.md). 2. **Scenario 2 – building a user interface for your agent** Build a console-based client that talks to your agent over the AG-UI protocol, so you can send user input and receive agent responses without building a full web UI. See [Scenario 2 README](src/scenarios/02-building-agent-ui/README.md). 3. **Scenario 3 – exposing your agents to other agents** Expose a weather agent over the A2A protocol and connect it to a separate travel-planning agent that calls it remotely to plan 5-day trips only to locations with good weather. See [Scenario 3 README](src/scenarios/03-connecting-two-agents/README.md). 4. **Scenario 4 – orchestrating a workflow across multiple agents** Use deterministic workflows to control the order in which several agents (preference collection, location suggestion, weather checking, summarization) collaborate to create a travel plan. See [Scenario 4 README](src/scenarios/04-orchestrating-agents/README.md). 5. **Scenario 5 – declarative agents and workflows** Recreate the travel planning and weather validation flow using declarative agent and workflow definitions instead of imperative code, to understand low-code orchestration patterns. See [Scenario 5 README](src/scenarios/05-declarative-agents/README.md). 6. **Scenario 6 – moderating a discussion between agents** Design a multi-agent travel system (places, weather, activities, flights, hotels) coordinated by a moderator/orchestrator such as Magentic One, enforcing global rules like budget, preferred locations, and activity diversity. See [Scenario 6 README](src/scenarios/06-moderating-agents/README.md). 7. **Scenario 7 – agent observability and evaluation** Enable OpenTelemetry-based tracing and metrics for one of your agents, wire it to an observability backend if available, and use evaluation loops and custom metrics to analyze and improve behavior. See [Scenario 7 README](src/scenarios/07-observability/README.md). ## Troubleshooting - **Missing environment variables** — Verify `.env` mirrors the keys called out. - **Model deployments** - Make sure the model deployment names are matched in the env file. Notice that GitHub model deployment names are starting with a provider prefix "openai/gpt-5-nano" instead of "gpt-5-nano". - **Application Insights ingestion delay** — Telemetry can take a few minutes to appear in the Azure portal; use the Live Metrics Stream for near-real-time debugging. ## Additional Resources - Product documentation: - GitHub repository: - Microsoft AI guidance: ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [denniszielke](https://github.com/denniszielke) - **Source:** [denniszielke/frontier-agents-workshop](https://github.com/denniszielke/frontier-agents-workshop) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-denniszielke-frontier-agents-workshop - Seller: https://agentstack.voostack.com/s/denniszielke - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.