Self-Improvements in Modern Agentic Systems: A Survey

2026-07-14

AI Summary

This work investigates how agents can achieve controllable, continual evolution and adaptation with minimal human intervention. We model self-improving agents as operational scaffolds comprising a foundation model integrated with prompts, memory, tools, and control logic, and formalize self-improvement as self-triggered updates to either model parameters or scaffold components. Based on the update objectives and driving signals, we propose the first systematic taxonomy that unifies existing approaches, clarifies application scenarios and evaluation metrics, identifies key open challenges, and establishes a dynamically maintained repository of technical advances in the field.

Abstract

Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains. We offer a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic. Within this framework, self-improvement is formalized as a self-induced update operator that obtains and commits updates to model parameters or scaffold components. We organize prior work by update target and by the signals that drive change, then review applications and discuss evaluation, before closing with open problems and future directions. For convenience, we track technical updates on https://github.com/selfimproving-agent/awesome-Self-Improving-Agents.

Problem

• self-improvement
• autonomous agents
• controllable evolution
• adaptive systems
• experience-driven adaptation

Innovation

• self-improving agents
• adaptive systems
• foundation models
• update operator
• operational scaffold