Self-Improvements in Modern Agentic Systems: A Survey

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 this GitHub page.

Introduction. The development of artificial intelligence (AI) technology has driven a paradigm shift in agentic systems (Shoham, 1993; Maes, 1994; Wooldridge & Jennings, 1995; Yao et al., 2022; Wang et al., 2024a), from earlier narrow systems built around task-specific models or hand-engineered modules to modern agentic systems powered by foundation models (FMs), including large language models (LLMs) and vision-language models (VLMs), where natural language serves as a shared interface for representation, reasoning, and control. Progress in foundation models has produced a qualitative shift in generalization, yielding striking successes across a wide range of domains, most notably code generation (Chen et al., 2021), language understanding (Hendrycks et al., 2020), and mathematical and formal reasoning (Wei et al., 2022). These advances have brought a long-standing question to the foreground: the prospect of AI systems that improve themselves. Fundamentally, self-improvement is an inherently self-referential process.

Discussion / Conclusion. Self-improving agents are closed-loop dynamical systems (Xie et al., 2024). According to our formulation, an agent At = (θt, Σt) progresses by executing a signal-generation procedure and applying a stable update rule to induce new policy. Consequently, the goal of the study is no longer the static agent but the mechanism driving its evolution (Yampolskiy, 2015; Robeyns et al., 2025; Pan et al., 2025). Building on the rigorous evaluation protocols established in Section 8, we examine the architecture of such systems and outline key directions for future research. The vision for machines that can improve themselves is an enduring theme in artificial intelligence. Building upon early successful implementations, the advent of large foundation models has substantially broadened the scale, versatility, and scope of self-improving systems. This survey synthesized this paradigm shift through a unified systems lens, distinguishing two primary pathways: (1) foundation-model improvement as a parametric, slower loop (driven by intrinsic generative demonstrations, intrinsic evaluative feedback, or extrinsic exploratory experience), and (2) scaffolding improvement as a non-parametric, faster loop (updating prompts, memory, tools, and full scaffolds).