# az prototype

An Azure CLI extension that enables users to rapidly build Azure proof-of-concept deployments using AI-driven agent teams. It implements a condensed version of the [Innovation Factory](https://github.com/microsoft/Innovation-Factory) POC methodology that Microsoft sales uses internally — 4 stages, 20 agents, production-quality output.

**Why use it?**

- Go from idea to deployed Azure prototype in minutes, not days
- AI agents handle architecture, IaC generation, deployment, and troubleshooting
- Governance policies, anti-patterns, and standards enforce best practices automatically
- Every stage is re-entrant — iterate and refine without starting over

---

## Pipeline

The CLI condenses the Innovation Factory's 12 stages into 4 core commands:

```
init  ──>  design  ──>  build  ──>  deploy
```

| Stage | What it does |
|-------|-------------|
| **init** | Create project folder, scaffold configuration (`prototype.yaml`) |
| **design** | Interactive discovery conversation, requirements analysis, architecture design, deployment planning |
| **build** | Generate IaC (Bicep or Terraform) and application code from the design |
| **deploy** | Run preflight checks, deploy infrastructure, capture outputs, run verification |

Each stage is re-entrant. Re-run `design` after deployment to refine architecture based on feedback, or re-run `build` to regenerate code after design changes.

---

## Features

- **20 AI agents** — 5 architects (cloud, infrastructure, data, application, security), 3 language-specific developers (C#, Python, React) + generic fallback, 2 IaC agents (Terraform, Bicep), and 9 supporting agents (QA, cost, docs, monitoring, governance, advisory, biz-analyst, project-manager, security-reviewer)
- **5 workload templates** — web-app, serverless-api, microservices, ai-app, data-pipeline
- **Governance engine** — 58 policy rules, 40 anti-pattern checks, 38 design standards enforced during generation
- **Benchmark suite** — 14 quality benchmarks (B-INST through B-ANTI) for measuring AI-generated code quality, with HTML dashboard, PDF reporting, and trend tracking
- **TUI dashboard** — Rich interactive terminal UI for design, build, and deploy sessions
- **Cost analysis** — S/M/L tier estimation via the cost-analyst agent
- **Backlog generation** — Generate and push user stories to GitHub Issues or Azure DevOps
- **Four-level taxonomy** — Layer/Capability/Component/Resource hierarchy drives deployment ordering and agent ownership ([Layer Architecture](Layer-Architecture), [Application Architecture](Application-Architecture))
- **MCP integration** — Model Context Protocol plugin system for extending agent capabilities
- **Knowledge system** — Runtime documentation, web search, and self-learning contributions
- **Error analysis** — QA-first troubleshooting with automatic escalation
- **Docs and spec kit** — Generate project documentation and stakeholder-ready specification packages

---

## Quick Start

```bash
# 1. Initialize a new prototype project
az prototype init --name my-poc --location eastus

# 2. Run interactive design session (discovery + architecture)
az prototype design

# 3. Generate infrastructure-as-code and application code
az prototype build

# 4. Deploy to Azure
az prototype deploy
```

See [Installation](Installation) for setup instructions and [Quickstart](Quickstart) for a full walkthrough.

---

## Command Reference (Summary)

| Command Group | Commands |
|--------------|----------|
| `az prototype` | `init`, `launch`, `design`, `build`, `deploy`, `status` |
| `az prototype analyze` | `error`, `costs` |
| `az prototype config` | `init`, `show`, `get`, `set` |
| `az prototype generate` | `backlog`, `docs`, `speckit` |
| `az prototype knowledge` | `contribute` |
| `az prototype agent` | `list`, `add`, `override`, `show`, `remove`, `update`, `test`, `export` |

See [Command Reference](Command-Reference) for full details on parameters and usage.

---

## Innovation Factory Stage Mapping

This CLI condenses the Innovation Factory's 12 detailed stages into 4 re-entrant stages:

| CLI Stage | IF Stages | Purpose |
|-----------|-----------|---------|
| `init` | -- | Project folder initialization, config scaffolding |
| `design` | 1-6 | Discovery conversation, requirements analysis, architecture design, deployment planning |
| `build` | 7 | Generate IaC (Bicep/Terraform) and application code |
| `deploy` | 8-10 | Infrastructure deployment, app deployment, customer testing |
| `design` (re-run) | 10-11 | Refinements based on feedback, architecture improvements |

---

## Navigation

| Section | Description |
|---------|-------------|
| [Installation](Installation) | Prerequisites, extension install, AI provider setup |
| [Quickstart](Quickstart) | End-to-end walkthrough from init to deploy |
| [Stages](Stage-Init) | Detailed documentation for each pipeline stage |
| [Configuration](Configuration) | `prototype.yaml`, secrets, AI providers, naming strategies |
| [Agent System](Agent-System) | Built-in agents, custom agents, overrides, governance |
| [Layer Architecture](Layer-Architecture) | Four-level taxonomy, deployment ordering, layer ownership |
| [Application Architecture](Application-Architecture) | App sub-layers, developer delegation, project structure |
| [Templates](Templates) | Workload templates and customization |
| [Backlog Generation](Backlog-Generation) | Generate and push stories to GitHub/Azure DevOps |
| [MCP Integration](MCP-Integration) | Extend agents with Model Context Protocol plugins |
| [Troubleshooting](Troubleshooting) | Common issues and solutions |