AI agents often struggle with Python projects, installing dependencies incorrectly or ignoring virtual environments. This leads to broken setups and wasted developer time. PyCharm's new Agent Environment Coordinator skill dramatically improves AI agent performance in Python development. It allows agents to access and utilize the project's specific Python environment.In tests, this skill boosted average task success rates from 68% to 98% across six AI models and 28 Python tasks. Crucially, these agents no longer pollutes the system Python environment. Previously, AI models failed to recognize project-specific interpreters, leading to global installations and script failures. The Agent Environment Coordinator enables agents to query PyCharm for the correct Python interpreter and its managing tool.If no environment exists, it can configure one using PyCharm's existing mechanisms. The skill provides context without taking control, allowing the agent to construct commands itself. This means agents work with existing project setups out of the box, eliminating the need for manual coaching or cleanup. This functionality is available with a JetBrains AI subscription. The methodology involved 28 everyday Python environment tasks, with success and system cleanliness being key metrics. The results clearly show that AI models simply lacked the necessary context, not capability.