https://github.com/denniszielke/frontier-agents-workshop
Microsoft Agent Framework - learn how to adress agentic enterprise scenarios
https://github.com/denniszielke/frontier-agents-workshop
a2a agents mcp microsoft-agent-framework
Last synced: 4 months ago
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Microsoft Agent Framework - learn how to adress agentic enterprise scenarios
- Host: GitHub
- URL: https://github.com/denniszielke/frontier-agents-workshop
- Owner: denniszielke
- License: mit
- Created: 2025-11-24T08:36:53.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-03T18:29:03.000Z (6 months ago)
- Last Synced: 2025-12-04T03:00:21.598Z (6 months ago)
- Topics: a2a, agents, mcp, microsoft-agent-framework
- Language: Shell
- Homepage:
- Size: 8.41 MB
- Stars: 15
- Watchers: 1
- Forks: 31
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
### đź› Â 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: