Google DeepMind's Evo2 Co-evolves Agent Harnesses and Weights
The key implication is that logged harness changes and their results can be recycled into training data, so teams already tracking agent iterations hold an untapped asset. Builders of coding agents and harness engineers can apply the Proposer-Reflector loop or adopt the released harnesses directly. The paper (arXiv 2610.13025) benchmarks against GPT-5-mini, giving a concrete reference point for anyone weighing harness effort against model upgrades.
