Ecdysis speeds up AI-agent harness training, a paper summary reports

A post sharing the Ecdysis paper describes a method for improving agent harnesses—the supporting code around AI agents. It highlights two problems: testing changes is slow, and fixes can overfit by treating model failures as harness bugs. According to the summary, Ecdysis analyzes failures across tasks and repairs only recurring patterns, after several diagnostic roles agree on the change. The post reports training up to 1.84x faster than existing harness evolution methods and an 18.56% gain in reasoning accuracy for the resulting harnesses. It also reports better transfer across large language models, fewer tokens used and results matching full-data training with a quarter of the data.