GitLab Sprint Manager

An AI-powered GitLab project manager built with Google Cloud and Gemini that analyzes issues, prioritizes work, generates sprint plans, and automatically creates actionable GitLab tasks.

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Inspiration

Software teams often spend a significant amount of time manually reviewing issues, prioritizing bugs, planning sprints, and tracking project progress. I wanted to build an AI-powered assistant that could reduce this overhead by automatically analyzing GitLab repositories and helping teams make better planning decisions.

The idea behind GitLab Sprint Manager was to create an intelligent project management agent that can understand the current state of a GitLab project, identify priorities, generate sprint plans, and take actions directly within GitLab.

What it does

GitLab Sprint Manager is an AI-powered project management agent built using Google Cloud, Gemini, and GitLab.

The agent can:

  • Retrieve and analyze GitLab issues
  • Prioritize work based on issue type and severity
  • Generate sprint recommendations
  • Identify high-priority bugs and blockers
  • Summarize project status
  • Automatically create new GitLab issues for sprint goals and follow-up tasks

Instead of manually reviewing every issue, teams can ask the agent to analyze the project and generate actionable recommendations.

How we built it

The solution was built using:

  • Google Cloud
  • Gemini
  • Python
  • GitLab APIs
  • Google Agent Platform

Custom GitLab tools were developed to retrieve issues, create issues, and generate project summaries. The agent uses these tools to gather project information and perform actions directly within GitLab.

The workflow is:

  1. Read project issues from GitLab
  2. Analyze issue priorities
  3. Generate sprint recommendations
  4. Create actionable GitLab tasks automatically
  5. Present the results to the user

Challenges we ran into

One of the biggest challenges was integrating external project management workflows with AI agents while maintaining a simple and reliable user experience.

Another challenge was handling authentication and communication between the agent and GitLab services. Several iterations were required before the agent could successfully retrieve repository information and create new tasks automatically.

We also explored multiple approaches for integrating GitLab services with the agent architecture before arriving at a reliable solution.

Accomplishments that we're proud of

  • Successfully integrated GitLab project data into an AI workflow
  • Built an agent capable of analyzing real project issues
  • Automated sprint planning activities
  • Enabled automatic creation of GitLab issues
  • Created an end-to-end workflow from project analysis to action execution

What we learned

Through this project we learned more about:

  • Agent-based application design
  • AI tool orchestration
  • GitLab API integration
  • Google Cloud services
  • Building multi-step autonomous workflows

What's next for GitLab Sprint Manager

Future improvements include:

  • Automatic sprint estimation
  • Merge request analysis
  • Team workload balancing
  • Integration with calendars and notifications
  • Automated project health reporting
  • Advanced Gemini-powered planning and recommendations

GitLab Sprint Manager demonstrates how AI agents can move beyond simple conversations and actively assist teams by analyzing project data and taking meaningful actions inside development workflows.

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