Co-Harness: Co-Evolving Harnesses and Models for Agent Post-Training

Post-training tool-using agents involves both learning model parameters and designing the runtime harness that generates their interaction trajectories. When the harness remains fixed, model updates can change agent behavior without corresponding adjustments to prompts, tools, skills, middleware, and memory. We introduce Co-Harness, a framework that alternates harness optimization with model post-training. An LLM-based HarnessCritic analyzes failed trajectories and proposes local harness edits, which are retained after validation. The model is then fine-tuned on quality-filtered trajectories collected under the updated harness, and the resulting model guides the next round of harness search. We evaluate Co-Harness on repository-level coding benchmarks SWE-bench Verified and SWE-bench Pro, and on tool-integrated mathematical reasoning benchmarks AIME24, AIME25, and HMMT25. Performance improves over two joint rounds across the evaluated model–benchmark pairs. A separate 200+ hour case study with model weights fixed documents automated runtime repair, execution-efficiency changes, and ensemble exploration. Co-Harness incorporates adaptation of the trajectory-generating runtime into the model-training process.