Researchers introduce EAU, a meta-agent framework for co-evolving the environment, user simulator, and agent during multi-turn reinforcement learning. EAU diagnoses failures and proposes targeted modifications, enabling the training setup to evolve with the policy. This approach outperforms prior RL methods on three interactive-agent benchmarks, improving overall scores by 14.7-15.0% and increasing successful task completion on ColBench by up to 36.7%.