Ecdysis optimizes agent harnesses via failure aggregation

Treating agent failures as harness execution bugs rather than model shortcomings represents the right mental model for production agent engineering. As foundational model weights commoditize, execution scaffolding and control loops will dictate real-world agent reliability. Isolating harness bugs from stochastic model noise solves a major bottleneck in automated agent optimization by preventing overfitted prompt workarounds. Batch-level cross-instance failure aggregation surfaces reproducible defects across tasks instead of chasing noisy single-trial failures. The collaborative four-agent refinement workflow replaces brute-force iterative search with structured diagnostic specifications and targeted code edits. Achieving up to a 1.84x training speedup while requiring one-quarter of typical training data makes automated harness evolution practical for resource-constrained teams.