EvoCost: Cost-Effective Agents via Harness Self-Evolution

LLM agents can incur substantial inference cost on complex tasks that require long interaction trajectories. Existing cost-saving methods mainly compress accumulated history or control execution stopping, but do not change the behavior that generates unnecessary interactions. We introduce \sys, a framework for cost-effective harness self-evolution that learns from execution experience to reduce unnecessary interactions in future executions while preserving task performance. \sys first performs cost-saving opportunity discovery by reconstructing execution dependencies and combining them with execution outcomes to identify interactions that can be safely skipped, collapsed, batched, or redirected. It then performs validated harness evolution, translating these opportunities into reusable harness edits and retaining updates that reduce inference cost while meeting stage-dependent task-performance requirements. Across four benchmarks and three model backbones, \sys reduces inference cost relative to the original harness in all 12 settings by 22.7% on average, while improving or preserving task performance in 10 of them. These results demonstrate that harness self-evolution can provide a practical path toward more cost-effective agent execution.