Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
Scrouting: Cost-Aware Routing of Coding Agents by Scouting the Repository First
TL;DR
SuperScout is a cost-effective routing system for coding agents that first scouts a repository before dispatching tasks. It achieves a high solve rate at a significantly lower cost compared to traditional models.
✦ Why It Matters
Consider implementing SuperScout to optimize task routing and reduce costs in your coding projects.
Key Takeaways
- Explore using SuperScout for efficient task routing.
- Leverage the structured handoff to improve coding agent performance.
- Test the system on your own repository for cost savings.
- Consider adding new fixers without retraining the model.
How It Works
SuperScout first scouts the repository using a 7B parameter model to gather context and verify claims. It produces a structured handoff that informs a resume-based router, which then selects the most suitable fixer for the task.
This approach allows for efficient routing based on the repository's state rather than solely on the issue text.