EvoGenome: Genotype-Phenotype Evolution for Compositional Agent Skills

Large language model agents increasingly use reusable skills that encode procedural knowledge for tool use, artifact manipulation, verification, and failure recovery. Existing skill-evolution methods search over free-form text, files, or complete skill packages. As a result, a single edit can modify unrelated behaviors, which complicates the attribution and recombination of successful changes. We introduce EvoGenome, a genotype–phenotype framework that represents compositional agent skills as typed, functionally defined, and independently heritable units. Its SkillGenome encodes activation triggers, preconditions, planning logic, tool-use policies, executable modules, postconditions, verification procedures, recovery behavior, and adaptation interfaces. A materialization compiler translates each genome into a conventional executable skill package, separating structured search from deployment while maintaining compatibility with existing agent harnesses. Type- and interface-aware operators perform constrained mutation, homologous crossover, module specialization, pruning, and cross-domain transplantation while preserving dependencies among skill components. EvoGenome uses execution traces and lineage differences to attribute behavioral and fitness changes to inherited genome segments, allowing subsequent variation to prioritize genes that provide consistent benefits. We compare EvoGenome with free-form rewriting and file-level crossover on a skill benchmark and a dynamic tool-use environment under fixed execution budgets. We evaluate validity, executability, behavioral innovation, fitness improvement, regression, and cross-domain transfer. Ablation studies assess the effects of type constraints, lineage-based credit, crossover, non-textual genes, and genome granularity.