Understanding Effectiveness, Efficiency, and Identifiability in Agentic Evolution: A Control-Theoretic Perspective

Agentic evolution improves agent systems through iterative evaluation and patching, yet existing work primarily focuses on performance, leaving the dynamics of the evolution process insufficiently understood. We present a control-theoretic framework for understanding agentic evolution as a discrete-time closed-loop system, which analyzes three properties that directly characterize the evolution process, including: *(i) Effective* evolution favors a smaller patch radius with stronger local error contraction, *(ii) Efficient* evolution benefits from lower initial error, faster contraction, and a smaller patch-to-error ratio, and *(iii) Identifiable* evolution requires stronger observable patch effects, lower effect coherence, and fewer simultaneously active patch directions. Experiments on three mainstream agentic benchmarks validate these theoretical findings. Guided by these findings, we propose EvoTrust, a trust-region framework that explores failure-driven patch directions and adaptively constrains patch size and component-edit scope according to observed outcomes. EvoTrust outperforms the strongest baseline by 3.9 percentage points on average while using 16.5% fewer evolution tokens overall, demonstrating its effectiveness and efficiency.