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Learn-Microsoft-Agent-Framework-with-Foundry-ZavaShop-Supply-Chain-Workshop

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ZavaShop supply-chain workshop: 5 LABs on Microsoft Agent Framework + Microsoft Foundry, with shared data fixtures, AG-UI control tower, and a GitHub Copilot coding agent.

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# Learn Microsoft Agent Framework with Foundry — ZavaShop Supply‑Chain Workshop

> A Chinese edition of every README in this workshop is preserved alongside as `README.zh.md`.

This workshop walks you through the **[Microsoft Agent Framework](https://learn.microsoft.com/en-us/agent-framework/) + [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/foundry/agents/overview) (gpt-5.5)** stack in **five LABs**, each one delivering a runnable feature on top of a single, continuous story.

> **Two implementation tracks share the same story.** Every LAB ships in both **Python** and **.NET (C#)**. Same fixtures, same acceptance criteria, same Foundry model. Pick a track per LAB — you can switch between LABs.

---

## 0. The story — ZavaShop

**ZavaShop** is a fictional global e-commerce company. Its catalog covers three categories:

- 🏠 **Home goods** — bedding, decor, lighting
- 🌿 **Garden & outdoor** — garden tools, BBQ grills, outdoor furniture
- 🔌 **Small home appliances** — coffee machines, air purifiers, robotic vacuums

ZavaShop has **5 fulfillment centers** (Seattle SEA-01, London LON-02, Shanghai SHA-03, São Paulo SAO-04, Dubai DXB-05) and partners with **dozens of suppliers**. Over the past year, the same pain points keep surfacing:

| Domain | Pain point |
|--------|-----------|
| Warehousing | Stock numbers are inconsistent across systems; warehouse supervisors are blocked by repetitive questions |
| Procurement | Buyers manage 8+ ERP/supplier portals daily; quotes and contract clauses are scattered, with frequent approval errors |
| Customer service | Reps don't remember VIP preferences (white-glove delivery, no cardboard, time windows…); chat models drift off-topic |
| Fulfillment | Quote → stock check → approval → dispatch → finance is fully manual; one $10k+ exception per day |
| Operations | Leadership wants a single dashboard, but every team builds their own |

The CTO has signed off on a **Foundry-as-the-brain** charter:

> *Every team rebuilds its workflow on Microsoft Agent Framework + Microsoft Foundry. The model is **gpt-5.5**. Workflows must orchestrate cross-team. The frontend is **AG-UI + React**, so the CEO can see everything from one console.*

You are the **AI Platform Engineer** at ZavaShop. The five LABs below are the five deliverables of this initiative.

---

## 1. The five LABs

| LAB | Story | Owner | What you build | Python SKILL | C# SKILL |
|-----|-------|-------|---------------|--------------|----------|
| [LAB01 — Inventory Agent](workshop/LAB01-inventory-agent/README.md) | Seattle DC supervisor **Mei** gets interrupted 60× a day | Mei | Agent **Zara**: function tools + HostedMCPTool + Thread | [agent-framework-azure-ai-py](.github/skills/agent-framework-azure-ai-py/SKILL.md) | [agent-framework-azure-ai-csharp](.github/skills/agent-framework-azure-ai-csharp/SKILL.md) |
| [LAB02 — Procurement Toolbox](workshop/LAB02-procurement-toolbox/README.md) | Shanghai senior buyer **Pierre** juggles 8 systems | Pierre | Agent **Pierre**: Foundry Toolbox + Agent Skills + approval workflow | same as above (Toolbox / Skills / Threads) | same as above (Toolbox / Skills / Threads) |
| [LAB03 — Customer Memory & Eval](workshop/LAB03-customer-memory-eval/README.md) | CS director **Lin** wants an agent that "remembers customers and is measurable" | Lin | Agent **Aria**: Foundry Memory + Evaluation + Red-Team | same as above (Memory / Evaluation) | same as above (**Memory only** — Evaluation + Red-Team SDKs are Python-only; reuse the Python scripts against your C# agent's endpoint) |
| [LAB04 — Fulfillment Workflow](workshop/LAB04-fulfillment-workflow/README.md) | Fulfillment director **Diego** wants exception orders to self-orchestrate | Diego | Multi-agent **ZavaFulfillment** workflow: WorkflowBuilder + HITL + Checkpoint | [agent-framework-workflows-py](.github/skills/agent-framework-workflows-py/SKILL.md) | [agent-framework-workflows-csharp](.github/skills/agent-framework-workflows-csharp/SKILL.md) |
| [LAB05 — Control Tower with AG-UI](workshop/LAB05-control-tower-agui/README.md) | The CEO wants a single dashboard that "feels alive" | CEO | **ZavaControlTower** AG-UI server + React frontend (covers all 7 AG-UI features) | [agent-framework-agui-py](.github/skills/agent-framework-agui-py/SKILL.md) | [agent-framework-agui-csharp](.github/skills/agent-framework-agui-csharp/SKILL.md) |

Every LAB ships:

- A **story setup** so you understand *why* you are building this
- A **task list** anchored to the SKILL's best practices
- **Acceptance criteria** so you can self-verify
- A **story handoff** that connects to the next LAB

---

## 2. Common prerequisites

### 2.1 Azure / Foundry

- An **Azure subscription** with the **Microsoft Foundry** service enabled
- A Foundry project with the **gpt-5.5** model + **text-embedding-3-small** deployed
- Local Azure CLI logged in:

```bash
az login --use-device-code
```

### 2.2 Local environment — pick a track (or both)

The core fixtures and the Coding Agent are language-agnostic. Pick whichever LAB tracks you want to run.

#### Python option

- Python **3.10+**
- Node.js **20+** (LAB05 frontend only)

```bash
python -m venv .venv
source .venv/bin/activate   # macOS / Linux
# .venv\Scripts\activate    # Windows PowerShell

pip install \
  agent-framework \
  agent-framework-azure-ai \
  agent-framework-ag-ui \
  azure-identity \
  python-dotenv \
  fastapi \
  "uvicorn[standard]"
```

#### .NET option

- **.NET 10 SDK** (`dotnet --version` ≥ `10.0.100`)
- Node.js **20+** (LAB05 frontend only)

The .NET track uses the prerelease Agent Framework NuGet packages — each C# SKILL lists the exact set per LAB. The common ones are:

```text
Microsoft.Agents.AI
Microsoft.Agents.AI.Foundry
Microsoft.Agents.AI.Workflows                          # LAB 4 / LAB 5
Microsoft.Agents.AI.Hosting.AGUI.AspNetCore            # LAB 5 server
Microsoft.Agents.AI.AGUI                               # LAB 5 client
Microsoft.Extensions.AI
Azure.AI.Projects
Azure.Identity
ModelContextProtocol                                   # MCP clients
```

Each LAB's `.csproj` links the shared data helper:

```xml
<ItemGroup>
  <Compile Include="..\..\data\ZavaData.cs" Link="ZavaData.cs" />
</ItemGroup>
```

> Individual LABs may add small extras (e.g. AG-UI client). Check each LAB's README for details.

### 2.3 `.env`

Create `.env` at the workspace root:

```dotenv
FOUNDRY_PROJECT_ENDPOINT=https://<your-project>.services.ai.azure.com/api/projects/<project-name>
FOUNDRY_MODEL=gpt-5.5
AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small

# Only required for LAB05
AGUI_SERVER_URL=http://127.0.0.1:5100/
AG_UI_API_KEY=zava-control-tower-demo-key
```

> Every LAB reads `FOUNDRY_MODEL` / `FOUNDRY_PROJECT_ENDPOINT` from the environment — **never hardcode** these in source files. The same env vars are used by both the Python and the .NET tracks.

---

## 3. Six SKILL files (three per track, read in order)

The Coding Agent always loads the SKILL for the chosen track. Python and C# SKILLs cover the same topics so you can swap tracks per LAB.

### Python

| SKILL | Use it for | Read it before |
|-------|-----------|---------------|
| [agent-framework-azure-ai-py](.github/skills/agent-framework-azure-ai-py/SKILL.md) | Building agents on Foundry / Azure AI Agents Service: tools, MCP, Toolbox, Agent Skills, Memory, Evaluation, Threads | LAB01, LAB02, LAB03 |
| [agent-framework-workflows-py](.github/skills/agent-framework-workflows-py/SKILL.md) | Multi-agent workflows: WorkflowBuilder, Executor, HITL, Checkpoint, ConcurrentBuilder | LAB04 |
| [agent-framework-agui-py](.github/skills/agent-framework-agui-py/SKILL.md) | AG-UI server / client: SSE endpoints, frontend / backend tools, HITL, state, Generative UI, predictive updates | LAB05 |

### .NET (C#)

| SKILL | Use it for | Read it before |
|-------|-----------|---------------|
| [agent-framework-azure-ai-csharp](.github/skills/agent-framework-azure-ai-csharp/SKILL.md) | Building agents on Foundry / Azure AI Agents Service: tools, MCP, Toolbox, Agent Skills, Memory, Threads | LAB01, LAB02, LAB03 (memory only) |
| [agent-framework-workflows-csharp](.github/skills/agent-framework-workflows-csharp/SKILL.md) | Multi-agent workflows: `WorkflowBuilder`, executors, HITL `RequestInfoAsync`, checkpoint resume, `AsAgent()` | LAB04 |
| [agent-framework-agui-csharp](.github/skills/agent-framework-agui-csharp/SKILL.md) | AG-UI server / client in ASP.NET Core: `MapAGUI`, `AGUIChatClient`, shared state, frontend / backend tools, HITL | LAB05 |

> Every LAB README starts by saying *"Read the SKILL first."* **Do not skip.**

---

## 3.5 Shared ZavaShop data ([`workshop/data/`](workshop/data/README.md))

All five LABs read the same fictional dataset from [`workshop/data/`](workshop/data/) instead of inlining mock dicts. That keeps stock numbers, PO IDs, customer preferences and freight rates consistent across labs:

| File | Used by | Highlights |
|------|---------|-----------|
| `warehouses.json` | LAB01 / LAB04 / LAB05 | 5 fulfillment centers (`SEA-01`, `LON-02`, `SHA-03`, `SAO-04`, `DXB-05`) |
| `skus.json` + `inventory.json` | LAB01 / LAB04 | 10 SKUs across home / garden / appliance + on-hand-by-warehouse rows |
| `purchase_orders.json` | LAB01 / LAB02 | 6 POs covering every common status |
| `suppliers.json` + `contracts.json` | LAB02 | 8 suppliers + 5 framework contracts (MOQ, max single-PO ceiling, tier discounts) |
| `customers.json` + `orders.json` | LAB03 / LAB04 / LAB05 | 4 customer profiles (3 VIP) + 6 cross-LAB orders |
| `carriers.json` | LAB04 / LAB05 | 5 freight carriers (FedEx / DHL / USPS / Aramex / SF Express) |
| `exceptions.json` | LAB05 | 4 open exception cases for the control tower |
| `eval_queries.jsonl` | LAB03 | 5 evaluation prompts with expected tool + expected outcome |
| `zava_data.py` | every Python LAB | Loader module (`find_stock`, `find_po`, `find_supplier`, `find_contract`, `find_customer`, `find_order`, `load_*`) |
| `ZavaData.cs` | every .NET LAB | Loader module for the .NET track. Mirrors `zava_data.py`; exposes static `Load*` / `Find*` under namespace `ZavaShop.Workshop.Data`. Each LAB's `.csproj` links it as a shared compile. |

In every Python LAB script, add the data folder to `sys.path` and call the loader:

```python
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "data"))
from zava_data import find_stock, find_po
```

In every .NET LAB project, link the shared helper into the csproj and use it directly:

```xml
<ItemGroup>
  <Compile Include="..\..\data\ZavaData.cs" Link="ZavaData.cs" />
</ItemGroup>
```

```csharp
using ZavaShop.Workshop.Data;

var stock = ZavaData.FindStock("SKU-7421", "SEA-01");
int onHand = stock?["on_hand"]?.GetValue<int>() ?? 0;   // 312
```

See [workshop/data/README.md](workshop/data/README.md) for the full schema and editing rules.

---

## 4. GitHub Copilot Coding Agent + SKILL convention

This workshop ships a Coding Agent ready for GitHub Copilot Chat:

- Definition: [.github/agents/zavashop-coding-agent.agent.md](.github/agents/zavashop-coding-agent.agent.md)
- Activation: open **Agent Mode** in VS Code Copilot Chat → open the **Agent picker** → select **`zavashop-coding-agent`**.

It already knows the LAB ↔ SKILL routing and the **track ↔ SKILL** routing. For every LAB, the very first step in the README is to pick the agent from the dropdown and send a plain-text prompt that always states (a) the LAB number and (b) the language:

```text
I'm doing LAB X in <Python|C#> — <one-line goal>
```

> **Do not type `@zavashop-coding-agent`.** The agent is chosen from the Agent Mode dropdown; the chat text is plain task description. If you omit the language, the agent will ask once and then stick with your choice for the LAB.

When you do that, the Coding Agent will:

1. Look up the LAB ↔ SKILL routing table for your track.
2. `read_file` the matching SKILL into context (Python or C#).
3. `read_file` the LAB README (Python section or .NET section).
4. Generate the task plan, write the code, run validation.
5. Map each acceptance criterion in your LAB and report which ones pass.

> Always start a LAB by switching from the default Copilot Chat agent to `zavashop-coding-agent` — this guarantees the SKILL is loaded before any code is written.
>
> The Coding Agent will reply in your language (English by default). If you write Chinese, it will switch automatically.

---

## 5. Suggested order

```text
LAB01  →  LAB02  →  LAB03  →  LAB04  →  LAB05
single   tools+    memory+   workflow   AG-UI
agent    Skills    Eval      (HITL)     frontend
```

- LAB01 → LAB02 → LAB03 progress from a single agent to a "real" agent (tools, Skills, memory, evaluation).
- LAB04 lifts the single agent up to a **multi-agent workflow** (with stock-check, shipping-quote, approval — reusing LAB01's tool).
- LAB05 publishes the LAB04 workflow as an **AG-UI control tower** for end-user interaction.

You can do them in 5 short sessions or in one long workshop day — your call.

---

## 6. Directory layout

```text
MAF_Foundry_Foundation_Workshop/
├── README.md                                  # ← you are here
├── README.zh.md                               # Chinese edition
├── .github/
│   ├── agents/
│   │   └── zavashop-coding-agent.agent.md   # GitHub Copilot Coding Agent (Python + .NET aware)
│   └── skills/
│       ├── agent-framework-azure-ai-py/         # 🐍 Python track
│       ├── agent-framework-workflows-py/
│       ├── agent-framework-agui-py/
│       ├── agent-framework-azure-ai-csharp/     # 🟦 .NET track
│       ├── agent-framework-workflows-csharp/
│       └── agent-framework-agui-csharp/
└── workshop/
    ├── data/                                  # 🔵 shared ZavaShop fixtures (see data/README.md)
    │   ├── zava_data.py                       # Python loader
    │   ├── ZavaData.cs                        # .NET loader (linked into each LAB csproj)
    │   ├── warehouses.json / skus.json / inventory.json
    │   ├── purchase_orders.json / suppliers.json / contracts.json
    │   ├── customers.json / orders.json / carriers.json
    │   └── exceptions.json / eval_queries.jsonl
    ├── LAB01-inventory-agent/
    ├── LAB02-procurement-toolbox/
    ├── LAB03-customer-memory-eval/
    ├── LAB04-fulfillment-workflow/
    └── LAB05-control-tower-agui/
```

---

## 7. Onwards

Set up your environment, then go to **[LAB01 — Inventory Agent](workshop/LAB01-inventory-agent/README.md)** to meet Mei, the warehouse supervisor desperate for help.

> One mantra to remember: **"Read the SKILL first."**

Learn-Microsoft-Agent-Framework-with-Foundry-ZavaShop-Supply-Chain-Workshop

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