SELF-EVOLVING AI AGENT WITH SKILL MEMORY

Abstract

The Self-Evolving AI Agent with Skill Memory is an intelligent software system designed to improve its ability to perform tasks by learning from previous interactions, successful task executions, user feedback, and stored skills. Traditional AI assistants generally respond to individual requests without maintaining a structured memory of how tasks were solved previously. The proposed system introduces a skill-memory mechanism that allows the agent to reuse useful knowledge and improve its future performance. The system receives a users task through a conversational interface and analyzes the objective, required actions, available tools, and relevant context. An agentic reasoning module creates a plan and selects appropriate tools or previously learned skills. After completing the task, the system evaluates the result and stores successful procedures, reusable workflows, and important task information in a structured skill memory. The skill-memory layer contains reusable skills, task procedures, tool configurations, previous outcomes, and performance information. When a similar task is received in the future, the agent can retrieve relevant skills and adapt them to the current situation. This reduces repetitive reasoning and allows the agent to improve its efficiency over multiple interactions. The proposed system also includes feedback and evaluation mechanisms that - 3Edetermine whether a task was completed successfully. Failed or low-quality attempts can be analyzed to identify improvements, while successful workflows can be converted into reusable skills after appropriate validation. Human approval can be included before newly learned skills are permanently added or used for important operations. Overall, the Self-Evolving AI Agent with Skill Memory provides a framework for creating AI agents that become more capable through controlled learning and experience. It can be applied to software development, IT support, research, productivity, business automation, and other task-oriented environments. Future enhancements can include multi-agent skill sharing, automatic skill generation, reinforcement learning, multimodal memory, long-term planning, and advanced skill verification.