AI-Powered Issue Triage: From Chaos to Clarity in Seconds
Abstract
Tired of manual issue triage? Watch AI transform it from hours to seconds with an open-source system that auto-analyzes issues (GitHub, with Jira integration on the way), identifies root causes, suggests code fixes, and detects duplicates — all using your actual codebase as context.
Learn how we built it using Repomix, Google Gemini, and more. See it running on GitHub Actions, and explore our vision for a multi-platform bot service.
Real code, real analysis, real impact.
Description
Maintainers and engineering teams across GitHub and Jira spend a huge chunk of their time — often 40% or more — manually triaging issues. Reading unclear bug reports, searching the codebase, asking follow-up questions, hunting for duplicates.… all of this consumes hours every week.
In the Ansible ecosystem alone, this leads to hundreds of hours of repetitive triage work every month.
Our AI system functions like a skilled triage engineer, currently integrated with GitHub (with Jira and other platforms coming soon).
What It Can Do
- Understands issues in context using the entire codebase
- Finds likely root causes and affected components
- Suggests concrete code fixes (file + line references)
- Auto-labels issues with type + severity
- Detects duplicate issues using semantic similarity
- Protects against prompt injection
- Runs only when triggered (labels / conditions)
Supported Platforms
- Today: GitHub Actions
- 2026: Jira, GitLab via a Bot-as-a-Service model
Key Components
- Repomix for comprehensive codebase snapshots
- Gemini 2.0 Flash (Pro/Exp configurable)
- GitHub Actions integration layer
- Security engine for prompt-injection detection
- Duplicate finder + retry logic for stable outputs
Why This Matters
- This is a working system
- Teams can adopt the workflow immediately
- Saves time and reduces repetitive triage
- Improves consistency and reduces burnout
- Transparent — runs entirely inside GitHub Actions
This talk explains how our AI Issue Triage solution brings context-aware intelligence into developer workflows by combining code analysis, classification, root-cause detection, and automated suggestions — all inside GitHub.
I’ll conclude with a live demo on a real GitHub project showing an end-to-end automated triage.
Audience Takeaways
- How to integrate AI into existing dev workflows
- Practical prompt engineering for code analysis
- How to build secure AI systems (prompt injection defense)
- How to scale from a tool → service architecture
- Real-world benefits of AI automation
Target Audience
- Primary: Open-source maintainers, DevOps engineers, platform teams
- Secondary: Developers interested in AI/ML
- Experience Level: Intermediate (GitHub, Python, CI/CD familiarity)
Additional Information
Session Materials
- GitHub Repository: https://github.com/tanwigeetika1618/AI-Issue-Triage
- Live demo environment.
- Slides will include architecture diagrams, code snippets, and results
Engagement Plan
- High-engagement live demo
- Real-time issue analysis
- Invite audience to try it afterward
- Provide setup guide + documentation