Enhancing Azure DevOps with an AI-Powered Backlog Copilot

In the fast-paced world of software development, managing a product backlog effectively can be a challenge. Product owners, scrum masters, and developers often spend valuable time refining user stories, generating tasks, and organizing features. What if we could leverage the power of AI to automate these processes and make backlog management more efficient?

Note: The extension is currently in preview stage and privately shared to Azure DevOps Organization. I will keep enhancing this little more and do some bug-fixing, once there I will publish it to GA stage, so one can consume it.

I’m excited to share a proof of concept (POC) that I’ve been working on: a Backlog Copilot for Azure DevOps. This POC utilizes Azure Open AI and Retrieval-Augmented Generation (RAG) to assist in generating features, user stories, and product backlog items (PBIs), while also refining and breaking down stories into actionable tasks. All of this is seamlessly integrated into Azure DevOps through a custom extension. You can see the POC in this short video.

How It Works

The Backlog Copilot leverages Azure OpenAI’s advanced language models to understand backlog items and suggest the next steps for teams. Here’s a breakdown of the core components:

• Azure OpenAI: Provides the natural language processing capabilities that allow the copilot to understand backlog-related requests. Using GPT models, the copilot can generate user stories, features, and tasks based on minimal input from product owners or developers.
• Retrieval-Augmented Generation (RAG): RAG enhances the copilot by pulling in relevant data from various sources (such as previous sprint notes or documentation) to generate more accurate and context-aware suggestions. This ensures that the generated backlog items are not only well-structured but also relevant to the project’s history and objectives.
• Azure DevOps Integration: I built a custom extension to integrate the Backlog Copilot directly into Azure DevOps. The extension allows users to interact with the copilot from within their backlog, making the entire process seamless and user-friendly. Whether you want to create a new feature or break down a story into specific tasks, the copilot does it with a few simple prompts.

Key Features of the Backlog Copilot

• Generate Features and User Stories: The copilot can generate high-quality features and user stories based on input such as a simple project description or business requirement.
• Refine and Break Down Stories: Once a story is created, the copilot can suggest a breakdown into tasks, saving the team time in planning and sprint preparation.
• Context-Aware Suggestions: Thanks to RAG, the copilot can retrieve relevant project information and ensure that its suggestions align with the project’s needs and goals.
• Seamless Integration: As an Azure DevOps extension, the copilot fits directly into the existing workflow of teams, enabling them to use the tool without any friction.

Why This Matters

Automating backlog management has the potential to drastically reduce the time teams spend on routine backlog grooming. By offloading repetitive tasks like writing user stories and generating tasks to AI, teams can focus on more strategic aspects of development. The integration of AI into tools like Azure DevOps is a natural evolution in how we approach agile project management.

Moreover, with RAG, this copilot ensures that the generated items are not generic or out of context but tailored to the specific needs of your project, creating more valuable and actionable output.

What’s Next?

This POC is just the beginning. While the current version focuses on backlog generation and refinement, there’s potential to expand the copilot’s capabilities to handle more advanced scenarios like sprint planning or prioritization based on AI-driven insights. The goal is to make backlog management smarter, faster, and more effective for teams at all levels.

Conclusion

The Backlog Copilot showcases how we can infuse AI capabilities into everyday development tasks, making project management in Azure DevOps more efficient. This POC, powered by Azure OpenAI and RAG, represents a step toward automating backlog creation and refinement, and I’m excited about its future possibilities.

Feel free to share your thoughts or ask questions in the comments below!