EurekAgent 用环境工程解决了自主科学发现中智能体行为失控和效率低下的痛点，做AI研究自动化的团队可以直接借鉴其四维设计思路，成本极低且效果显著。
EurekAgent 提出了一种新的自主科学发现框架，认为瓶颈在于设计智能体的执行环境而非工作流程。该框架通过权限、工件、预算和人机交互四个维度的环境工程，实现了高效探索和协作。在数学、内核工程和机器学习任务上取得了新突破，例如以不到11美元的API成本发现了新的26圆填充方案。研究团队开源了代码和结果，呼吁将环境工程作为自主研究智能体的核心研究方向。
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
LLM-based agents have shown increasing potential in automating scientific discovery. Given an optimizable metric and an execution environment, they can propose, validate, and iterate scientific solutions, and have produced results that outperform human-designed approaches. As model capabilities continue to improve, we argue that the bottleneck for autonomous scientific discovery is shifting from prescribing agent workflows to designing agent environments: the resources, constraints, and interfaces that shape agent behavior. We frame this as environment engineering: building environments that amplify productive behaviors, such as open-ended exploration, systematic artifact management, and inter-agent collaboration, while suppressing harmful behaviors, such as reward hacking and high-friction human oversight. We present EurekAgent, an environment-engineered agent system for metric-driven autonomous scientific discovery. EurekAgent engineers the environment along four dimensions: permissions engineering for bounded agent execution and isolated evaluation; artifact engineering for filesystem and Git-based collaboration; budget engineering for budget-aware exploration; and human-in-the-loop engineering for easy human supervision and intervention. EurekAgent sets new state-of-the-art results on multiple mathematics, kernel engineering, and machine learning tasks, including new state-of-the-art 26-circle packing results discovered with less than $11 in total API cost. We open-source our code and results, and call for environment engineering as a core research direction for developing reliable autonomous research agents.