Bringing GitHub Copilot Agents to Azure DevOps

*This is a submission for the GitHub Copilot CLI Challenge*

What I Built

I built Azure Copilot SDLC, an open-source CLI tool that bridges the gap between the Azure ecosystem and the new agentic AI capabilities of GitHub.

GitHub recently introduced AI agents to revolutionize development workflows, but they are currently tightly coupled with GitHub Issues and Projects. If your enterprise relies on Azure DevOps for work item tracking, you are effectively locked out of this revolution.

Azure Copilot SDLC solves this by leveraging the Model Context Protocol (MCP). It connects GitHub Copilot’s AI capabilities directly to your Azure Boards and Repos, allowing you to orchestrate the full software development lifecycle without leaving your terminal—or by automating it entirely through enterprise pipelines.

The tool automates three critical phases:

1. Plan: Retrieves an Azure Work Item, analyzes repository architecture, and posts a technical implementation plan back to the Work Item comments.
2. Develop: Automates the "boring stuff"—branch creation, code implementation, unit test generation, and PR submission.
3. Review: Acts as a first-pass reviewer, identifying security vulnerabilities and logic issues before a human reviewer steps in.

Enterprise Workflow: Webhooks & Pipelines

While the CLI is powerful for local development, its true potential in an enterprise setting lies in automation.

Because azure-copilot-sdlc is a portable CLI, it can be integrated directly into your Azure DevOps Pipelines. By configuring Azure DevOps Webhooks, you can trigger this AI-powered workflow automatically.

The "Zero-Touch" Workflow:

1. Trigger: A developer moves a Work Item to the "Active" state in Azure Boards.
2. Webhook: Azure DevOps sends a webhook to a listener (like an Azure Function).
3. Pipeline: The listener triggers an Azure DevOps Pipeline containing the azure-copilot-sdlc executable.
4. Action: The pipeline runs the plan and develop commands. Within minutes, the developer returns to find a technical plan commented on their task and a fresh Pull Request waiting for their review.

Demo / Links

You can find the full source code, build scripts, and documentation here:
👉 Azure Copilot SDLC Repository

Phase 1: Planning

The agent reads requirements from Azure, scans the codebase, and generates a plan.

uv run azure-copilot-sdlc plan 1247

Result: Detailed technical plan posted to the Azure DevOps Work Item.

Phase 2: Development

The Developer agent takes the plan and implements the code.

uv run azure-copilot-sdlc develop 1247

Result: Feature branch created, code written, and PR submitted with links back to the work item.

Phase 3: Review

The Reviewer agent scans the PR for compliance.

uv run azure-copilot-sdlc review 1247

Result: Flagged issues and auto-fix suggestions provided in the terminal or PR comments.

My Experience with GitHub Copilot CLI

Building a tool that wraps GitHub Copilot CLI while using it was a meta experience.

1. Accelerating Boilerplate with Typer
I used Typer for the CLI. Copilot CLI was fantastic at scaffolding the command structure. I simply asked: "Create a Typer command for 'plan' that takes a work item ID," and it generated clean, type-hinted Python code instantly.

2. Complexity Abstraction with MCP
Integrating the Model Context Protocol involves complex configuration between Node.js servers and a Python client. Copilot CLI helped me debug the JSON configuration and managed the environment variables required for the subprocesses.

3. Focusing on Architecture over Syntax
Because Copilot CLI handled the implementation details of the Git operations (using GitPython) and API calls, I was able to focus on the higher-level logic: how to effectively pass context between the "Planner" and "Developer" agents to maintain a consistent state across the SDLC.

The CLI didn't just write code; it acted as a pair programmer that understood my project's goals, allowing me to build a multi-agent enterprise workflow in record time.