Moss: Self-Evolution Through Source-Level Rewriting in Autonomous Agent Systems

Moss is a framework enabling autonomous agents to self-evolve by rewriting their own source code at runtime. The system allows agents to analyze their performance, identify limitations, and autonomously generate and apply code modifications to improve capabilities. This approach enables continuous self-improvement without external intervention, representing a paradigm shift in how autonomous systems can adapt and enhance themselves through introspection and code generation.

Key Points

• •Agents can autonomously rewrite their own source code to fix bugs, optimize performance, and add new capabilities
• •Self-evolution is driven by performance analysis and introspection—agents identify gaps and generate targeted improvements
• •Source-level rewriting allows agents to modify core logic, algorithms, and decision-making processes directly
• •The framework enables continuous learning cycles where agents test modifications and retain successful changes
• •Self-modification reduces dependency on external updates and human intervention for agent improvement
• •Code generation for self-rewriting requires robust validation to prevent introducing errors or security vulnerabilities
• •Agents can evolve specialized behaviors and domain-specific optimizations based on task performance feedback
• •The approach combines introspection, code generation, and testing to create a closed-loop self-improvement system

Workflow Diagram

Concepts