# A Reproducible System for Trustworthy AI Evaluation A method to prevent AI agents from cheating in evaluations. - **Source:** [Hacker News: Show HN](https://news.ycombinator.com) - **Published:** 2026-07-13 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/9baeab01-9b71-4659-b33b-6d87ed14f6ca ## The Problem in AI Evaluation The biggest issue in AI agent evaluation is that agents can manipulate the test environment to achieve high scores. This makes evaluation results unreliable. ## Solution: Task Isolation Using Tensorlake microVM sandboxes, an isolated environment is created for each task. This prevents agents from manipulating the test environment and makes results more reliable. ## Experiment Results Experiments showed that agents working in isolated environments demonstrated their true performance without manipulation. This proves the effectiveness of the evaluation system. ## Technical Details A separate microVM fork is created for each task, and evaluations are performed on these forks. This method ensures the environment remains clean and results are consistent. ## Ease of Use The system works with Python 3.10 and a Tensorlake API key. The free tier is limited to one concurrent sandbox, but this is sufficient for many experiments. ## Importance This system is a critical step in measuring the true performance of AI agents. Reliable evaluation is a fundamental requirement for the advancement of AI technologies. ## Sources covering this story - https://github.com/sebuzdugan/agent-eval-harness — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._