Abstract
AI agents powered by LLMs plan, reason and use tools well, but remain brittle to adapt and evolve because they fail to abstract the mechanisms underlying problem-solving. Reasoning, memory, and decision-making remain fragmented, leaving the systematic reuse of experience for adaptation at scale out of reach. In this paper, we introduce EvA (Evolving Agents), a framework to improve evolution in agentic reasoning that leverages quasi-symbolic abstractions—semi-structured dynamic representations that furnish constructs to distil and reuse reasoning mechanisms. They are learned and refined through experience, and when instantiated, deliver meta-states for organising reasoning. EvA orchestrates this mechanism via a Perceptor modelling observation and action, an Actor conditioning its policy on these abstractions, and a Controller overseeing structure to explore paths or initiate rollbacks. As experience accumulates, EvA refines both its abstractions and the modules that construct and instantiate them by learning past trajectories into reusable mechanisms. Our initial analysis shows that EvA improves accuracy and adapts under mid-episode distribution shifts that cause agents to plateau on complex reasoning and interactive planning tasks. These results position EvA as a step towards adaptive reasoning, memory, and meta-control.