Google’s open source EnvHarness lets AI agents train against environments that evolve with them

Training agents for specific tasks such as coding or web navigation requires environments where they can safely practice, fail, and improve. But building these environments is expensive, and once they are created, they typically remain static even as the agent gets better.

Researchers from Google Cloud AI Research and academic partners have developed an open source (Apache 2.0 licensed) framework that turns these static environments into ones that can adapt to the weaknesses of the agent using them.

The framework, called EnvHarness, puts a programmable layer around an existing environment. It can change where an agent starts, what it sees, which actions it can take and how long a task lasts, while leaving the underlying environment and its verifier intact. 

Across five benchmarks covering software engineering, web navigation, office work and embodied tasks, agents learning from EnvHarness environments improved by up to 9 points on held-out tasks. On software engineering benchmarks, they also accomplished tasks in fewer steps than agents learning from the original environments.

For enterprise AI teams, the approach presents an alternative to continuously building new simulators and training tasks from scratch: start with a trusted environment and dynamically reshape it around the agent’s current weaknesses.