We Stopped AI Agents From Installing Into the Wrong Python: Task Success Rates Jumped to 95%+

JetBrains' PyCharm introduced the Agent Environment Coordinator skill, which provides AI agents with the correct Python interpreter information, boosting task success rates from 68% to 98% on average across six AI models tested on 28 Python programming tasks. The skill, available with a JetBrains AI subscription, allows agents to query PyCharm for the project's environment and configure interpreters without modifying the system Python.

We Stopped AI Agents From Installing Into the Wrong Python: Task Success Rates Jumped to 95%+ AI agents are supposed to save you time. Ask one to install a dependency or run your project, though, and it often does the opposite: It installs into the wrong Python, ignores the uv or virtual environment your project uses, and hands back a broken setup for you to fix yourself. PyCharm https://www.jetbrains.com/pycharm/ ’s new Agent Environment Coordinator skill fixes this, and this blog post shows just how helpful it proves to be. We tested six AI models using 28 different Python programming tasks. Without access to the project’s real environment, they solved 68% of the tasks on average. After we gave them access, their average success rate shot up to 98% – and they didn’t even modify the system Python. If you’re currently using AI agents in your Python projects, read on to see how the Agent Environment Coordinator can improve their performance. When the agent could see the project’s environment, it stopped failing When using the Agent Environment Coordinator skill, each agent, regardless of the model, was able to complete far more of the 28 tasks. See the Methodology section below for details on what the tasks entailed. Here is the share of successfully completed tasks for each model, comparing the baseline to running with the skill in PyCharm: Every model improved, with the weakest baseline improving the most. Why we built this LLMs almost never use a project’s dedicated virtual environment. They fall back to a system interpreter, ignoring the fact that there may be several system interpreters and real projects often have more complex, multi-interpreter setups already configured in PyCharm that the agent has no way to see. For example, pip install httpx runs against the wrong Python, the package installs globally, the script fails, and the environment is polluted. PyCharm already knows which interpreter belongs to your project and which tool manages it. The agent just couldn’t ask – so we gave it a way. How it works The Agent Environment Coordinator lets the agent ask PyCharm two things. get python environment returns the correct interpreter for the file or module in question – the path plus the tool behind it uv , Poetry, pip + venv, conda . If no environment exists yet, configure python interpreter sets one up by reusing PyCharm’s existing configuration mechanism – the same one that offers to create a .venv – so the new interpreter also becomes visible in the IDE. The important part is what the skill doesn’t do. It returns information; it never intercepts or rewrites the command. The agent asks which Python to use, gets an accurate answer, and decides whether and how to use it to write the command itself. We hand it the missing context using existing mechanisms in PyCharm – we don’t let it take the wheel. The payoff is practical: The agent works with your project setup out of the box. You don’t need to coach it through prompts about which environment to use, or clean up wrong installs afterward. This PyCharm functionality is available with a JetBrains AI subscription. Methodology We built a dataset of 28 tasks covering everyday Python-environment work, like running tests, installing a library, listing dependencies, resolving a version conflict, and so forth. Each task ultimately required the agent to pick the correct interpreter to execute a command. The eval also reduced the reward when the agent polluted the system environment, so a high score reflects a clean run, not just a passing one. We ran the full set three times per model, with and without the skill, using Harbor https://www.harborframework.com/ , and averaged the results. Results Success rates climbed across the board – Sonnet 5 improved from 73% to 100%, Opus 5 from 94% to 100%, and Codex/GPT-5.6 from 80% to 100%. Two things stand out in addition to this numerical jump: - The improved success rates demonstrate that the models lacked context, rather than being incapable of completing the tasks. The models didn’t get better – they just stopped guessing the interpreter, which is why the weakest baseline improved the most. - Because the eval docks points for polluting the system environment, these higher scores also imply cleaner runs. The agents didn’t just pass more often; they stopped leaving a mess behind. Want to try it? Open the AI chat in PyCharm 2026.2.1 https://www.jetbrains.com/pycharm/download/ and ask your agent to install a package or run something in your project – it’ll reach for the right interpreter on its own. The Agent Environment Coordinator is one of PyCharm’s bundled skills. You can browse and manage all of them right in the IDE, expand the built-in library with external registries like public GitHub repositories, or import skills you’ve already set up for Claude Code or Codex. Subscribe to PyCharm Blog updates