Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Data Science
Towards AI·21d ago·5 min read
How to Build an AI Agent That Keeps Learning From GitHub
TL;DR
AI agents often stagnate because they lack access to new workflows and tools found in GitHub repositories. A new 8-stage pipeline was developed to discover these workflows, convert them into reusable skills, and involve human oversight before deployment.
✦ Why It Matters
Engineers can implement this 8-stage pipeline to ensure their AI agents continuously learn from the latest GitHub workflows.
Key Takeaways
- Build an automated pipeline for continuous learning in AI agents.
- Use structured stages to ensure reliable workflow extraction.
- Implement human review to maintain quality and safety.
- Focus on acquiring new capabilities rather than model retraining.
How It Works
The pipeline consists of eight distinct stages, each handled by a specialized agent. It starts with a Scout that discovers relevant repositories, followed by a Filter that removes irrelevant ones.
The Reader extracts documentation before diving into code, while the Workflow Extractor identifies reusable processes. The Skill Scorer evaluates the quality of these workflows, and the Skill Generator standardizes them into a usable format.
A Reviewer assesses the skills before they are published through a Publisher, which opens a pull request for human approval.