- Introduce LLM-driven decision-making across various agents, allowing for dynamic adjustments based on real-time analysis.
- Implement new event types for LLM thinking, decisions, actions, and observations to enrich the event streaming experience.
- Update agent task responses to include additional metrics for better tracking of task progress and outcomes.
- Refactor UI components to highlight LLM-related events and improve user interaction during audits.
- Enhance API endpoints to support new event structures and improve overall error handling.
- Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification.
- Add API endpoints for managing Agent tasks and configurations.
- Implement UI components for Agent mode selection and embedding model configuration.
- Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search.
- Create a sandbox environment for secure execution of vulnerability tests.
- Update documentation to include details on the new Agent audit features and usage instructions.
lintsinghua