CodeAgent X

Inspiration

Software teams spend a significant amount of time fixing bugs, refactoring legacy code, and interpreting scattered error signals from logs, screenshots, and outdated architectures. Traditional static analysis tools only catch a subset of real issues, and most AI tools generate isolated snippets rather than understanding entire systems. We wanted to build an autonomous engineering agent capable of reasoning across full repositories, interpreting multimodal signals, and delivering production‑ready improvements. That vision became CodeAgent X.

What it does

CodeAgent X ingests an entire codebase, analyzes its structure, identifies issues, and generates high‑quality fixes and refactors. It interprets multimodal inputs such as screenshots of errors, logs, and architecture diagrams to diagnose problems that traditional tools miss. It then produces a prioritized repair plan and generates GitHub‑ready patches with clear explanations. CodeAgent X functions as an AI that engineers, not just an AI that writes code.

How we built it

We combined Gemini 3’s long‑context reasoning with static analysis tools, repository parsing, and a lightweight orchestration layer. Gemini 3 handles architecture understanding, multimodal interpretation, and patch generation. A backend service clones repositories, runs linters and tests, and feeds structured signals into the model. A patch generator formats diffs and pushes pull requests through the GitHub API. A simple UI or CLI allows users to upload screenshots, logs, or repository links.

Challenges we ran into

• Designing prompts that balance autonomy with safety and determinism
  
• Handling large repositories within context limits
  
• Converting multimodal signals into actionable insights
  
• Ensuring generated patches were correct, minimal, and review‑ready
  
• Integrating multiple analysis tools into a clean workflow
  

Accomplishments that we're proud of

• Building a system that performs true end‑to‑end refactoring rather than isolated code generation
  
• Achieving reliable multimodal debugging from screenshots and logs
  
• Producing clean, merge‑ready pull requests with clear explanations
  
• Creating a workflow that feels like working with a real autonomous engineer
  

What we learned

• Multimodal reasoning significantly improves debugging accuracy
  
• Long‑context models can understand architecture surprisingly well
  
• The challenge is not generating code, but generating safe, minimal, correct changes
  
• Developers value clarity, explainability, and collaboration from AI systems
  

What's next for CodeAgent X

• Full CI integration to automatically fix failing pipelines
  
• Deeper modernization support for frameworks and infrastructure
  
• Continuous monitoring agents that watch repositories and propose improvements
  
• A plugin ecosystem for language‑specific or domain‑specific repair modules
  
• A fully autonomous scheduled refactor mode
  

Built With

• antigravity
• gemini-3-pro