Technical Debt Hunter

Inspiration

Technical debt is one of the most persistent challenges in software development. As teams focus on delivering new features and meeting deadlines, maintenance activities such as refactoring, improving test coverage, and updating documentation are often postponed. Over time, these small compromises accumulate and make systems more difficult to understand, maintain, and extend. Our inspiration for Technical Debt Hunter came from observing how developers frequently struggle to identify where technical debt exists and which issues should be addressed first. With the introduction of GitLab Orbit and its ability to provide contextual understanding of a repository, we saw an opportunity to create an AI-powered solution that could help development teams proactively discover and manage technical debt before it becomes a major obstacle.

What it does

Technical Debt Hunter is an AI-powered GitLab Duo Agent that uses GitLab Orbit to analyze a repository and uncover potential sources of technical debt. Instead of relying on developers to manually inspect large codebases, the agent evaluates repository structures, dependencies, and relationships between components to identify areas that may negatively affect maintainability. It provides developers with contextual recommendations and prioritized findings, enabling teams to focus their efforts on the issues that have the greatest impact on code quality and long-term project health. By transforming hidden technical debt into actionable insights, Technical Debt Hunter helps teams make more informed maintenance decisions.

How we built it

We built Technical Debt Hunter on the GitLab Duo Agent Platform and integrated GitLab Orbit as the primary source of repository intelligence. The agent begins by analyzing the repository through Orbit, allowing it to understand the structure of the project and the relationships between different components. Once the repository context has been established, the agent evaluates patterns that may indicate technical debt, such as highly interconnected modules, areas with insufficient testing, or components that may be difficult to maintain. The collected information is then processed to determine which findings should be prioritized based on their potential impact. Finally, the agent generates recommendations that developers can use to guide refactoring and maintenance efforts. This workflow allows Technical Debt Hunter to provide meaningful assistance based on the actual context of a project rather than isolated code snippets.

Challenges we ran into

One of the biggest challenges we encountered was determining how to identify technical debt in a way that would be both useful and actionable. Not every warning or code smell represents a significant problem, and presenting too many findings can overwhelm developers rather than help them. We needed to carefully consider how to distinguish between minor issues and high-impact areas that deserve immediate attention. Another challenge involved designing an experience that aligned with the goals of the Showcase Track. Rather than creating a simple conversational assistant, we focused on developing a workflow that performs meaningful analysis and produces practical outcomes. We also spent time understanding how to best utilize GitLab Orbit so that our agent could leverage repository-wide context instead of relying on individual file analysis.

Accomplishments that we're proud of

We are proud that Technical Debt Hunter addresses a real problem faced by development teams and demonstrates how AI can contribute to software maintainability. We successfully created an agent that uses GitLab Orbit to understand repository context and provide recommendations that are grounded in the actual structure of a project. We are also proud that our solution focuses on actionable outcomes rather than simple analysis, helping developers identify where improvements can have the greatest impact. Most importantly, we built a solution that highlights the practical value of combining repository intelligence with AI-powered automation.

What we learned

Throughout this project, we learned that context is one of the most important factors in creating useful AI tools for software development. Access to repository-wide information allows an AI agent to provide more meaningful insights than traditional code analysis alone. We also gained valuable experience working with GitLab Duo Agent Platform and exploring how GitLab Orbit can be used to automate developer workflows. Beyond the technical aspects, we learned the importance of designing solutions around real user needs and ensuring that every recommendation generated by the system contributes to a clear and actionable outcome.

What's next for Technical Debt Hunter

Our vision for Technical Debt Hunter extends beyond repository analysis. In the future, we plan to enhance the agent with automated issue creation, continuous repository health monitoring, and deeper integration with development workflows. We also aim to introduce repository health scoring and historical tracking so teams can measure how technical debt evolves over time. Additional improvements may include intelligent refactoring suggestions, merge request risk assessments, and integration with CI/CD pipelines for continuous evaluation. By continuing to expand its capabilities, we hope Technical Debt Hunter will become a valuable companion for development teams seeking to maintain healthier, more sustainable codebases while reducing the long-term cost of technical debt.