Researchers introduced FlowEvo, a framework addressing the fact that workflows discovered by LLM agents during task execution are typically thrown away rather than reused. The system lets workflows and skills co-evolve at inference time, automatically converting successful procedures into callable skills stored in a persistent library. On benchmarks including ALFWorld and HumanEval, FlowEvo outperformed eight baseline methods while using roughly a third fewer tokens.
