Evolving Agent Workflows through Regular Expression Edits

Agent workflows combine language-model calls with verification, retries, and tools, but existing design methods often change control flow and module implementations together. Their search results therefore give limited evidence about which structural edits improve performance. We introduce reGROW, which fixes module implementations and searches a regular grammar over typed module calls. A compiler checks each candidate’s validity and resource bounds before execution. Two indices computed from the expression describe processing opportunity and control exposure. Starting from one reasoning call, reGROW applies explicit structural edits, uses a pretrained TabPFN predictor to prioritize evaluations, and uses paired measurements to decide which edits survive. An independent confirmation set selects the lowest-exposure finalist within a quality tolerance and empirical deployment-cost budget. Across seven benchmarks, reGROW improves average performance by 0.8% relative to the strongest reported baseline. Every retained edit carries a measured parent-child comparison.