Harnessing Agentic Evolution

2026-05-13

AI Summary

Existing agent evolution methods often suffer from rigidity that limits adaptability or drift from objectives over long-term evolution, while also struggling to systematically leverage historical evolutionary evidence. This work proposes AEvo, a novel framework that, for the first time, formulates agent evolution as an interactive environment. AEvo introduces a meta-agent that dynamically edits subsequent evolution mechanisms based on contextual evolutionary states, rather than directly generating candidate solutions, thereby unifying the control of both procedural and agent-based evolutionary processes. By incorporating evolutionary trajectory tracking and context management, the framework enables systematic reuse of historical evidence. Experiments demonstrate that AEvo outperforms five baselines across multiple reasoning and agent benchmarks, achieving a 26% improvement over the strongest baseline, and attains state-of-the-art performance under equivalent iteration budgets in three open-ended optimization tasks.

Abstract

Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed hand-designed procedures that are modular but rigid, or as general-purpose agents that flexibly integrate feedback but can drift in long-horizon evolution. Both forms accumulate rich evidence over time, including candidates, feedback, traces, and failures, yet lack a stable interface for organizing this evidence and revising the mechanism that drives future evolution. We address this limitation by formulating agentic evolution as an interactive environment, where the accumulated evolution context serves as a process-level state. We introduce AEvo, a harnessed meta-editing framework in which a meta-agent observes this state and acts not by directly proposing the next candidate, but by editing the procedure or agent context that controls future evolution. This unified interface enables AEvo to steer both procedure-based and agent-based evolution, making accumulated evidence actionable for long-horizon search. Empirical evaluations on agentic and reasoning benchmarks show that AEvo outperforms five evolution baselines, achieving a 26 relative improvement over the strongest baseline. Across three open-ended optimization tasks, AEvo further outperforms four evolution baselines and achieves state-of-the-art performance under the same iteration budget.

Problem

• agentic evolution
• evolutionary feedback
• long-horizon search
• meta-editing
• evolution context

Innovation

• agentic evolution
• meta-editing
• evolutionary framework
• interactive environment
• long-horizon optimization