Impressive research from Google on building better environments for agents.

Training environments for agents are hand-built and go stale. The agent improves, the environment does not, and it's not able to see the agent's weaknesses in the first place.

EnvHarness wraps a static environment in a programmable plug-in layer that reshapes its behavior without touching the underlying logic. Every reshaped environment keeps its original verifier; this is what makes the reshaping safe to train on.

EnvRigger treats the policy as a black box, reads its execution trajectories, synthesizes harness components aimed at the diagnosed flaws, then validates them with fresh rollouts.

Across five benchmarks in four domains, up to 9.0 points better on held-out instances with 9.8% fewer execution steps.

Paper: https://arxiv.org/abs/2608.19880

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