Self-Evolving AI Agents Through Skill Reusability

Summary & Methodology Analysis

FlowEvo introduces a three-stage lifecycle to optimize agent performance. First, it employs a workflow-to-skill compilation process that transforms successful inference-time workflows into structured skill records, essentially caching validated procedural logic. Second, a tiered reuse mechanism routes tasks either to direct skill execution or incorporates these skill records as contextual prompts during workflow generation. Third, the system utilizes a contrastive-utility lifecycle to evaluate the performance of these skills over time, actively suppressing those that contribute to negative transfer, which occurs when reuse of past data hinders current task performance. This loop enables the agent to progressively improve its efficiency as it accumulates a library of domain-specific procedures. The system was tested against eight baselines, consistently demonstrating superior accuracy across five diverse benchmarks covering interactive environments, coding, and mathematics. Testing across 10 base models with parameter counts from 7B to 671B shows that the framework provides reliable improvements in the majority of evaluated scenarios, specifically outperforming ExpeL in 49 out of 50 comparisons. FlowEvo is limited to settings where clear feedback is available to judge the success of an agent. It does not currently support free-form dialogue or tasks without clear success signals. Furthermore, the framework requires a base model with sufficient capability to initiate the self-evolution loop, meaning low-capacity models may fail to bootstrap the process.