Open machine learning engineering tasks require agents to interpret data, build models, debug code, and produce valid competition submissions. Although current coding agents can generate runnable programs, they often rediscover solutions across tasks and repeat engineering mistakes across feedback rounds. We propose EEMA, an experience-evolving meta-agent framework that organizes accumulated experience by purpose and routes it during multi-round solving. Its static component distills public competition material and historical failure traces into compact Skills: task-level modeling priors for improving solution quality and failure-prevention contracts for maintaining execution validity. Its dynamic component, Experience Routed Search (ERS), operates above a lower-level coding agent. At each round, ERS selects a draft, debug, or improve action, routes the corresponding Skill, and updates task memory, allowing cross-task experience to guide code generation while task-level feedback shapes later decisions. On MLE-bench Lite (22 tasks), EEMA achieves a 77.3% medal rate, 20.1% average leaderboard Grade, and 96.0% execution success, outperforming all baselines including GPT-5.5-xhigh. Ablations show that task Skills improve solution quality, failure-prevention Skills improve reliability, and ERS coordinates both effectively. The materials will be released at https://github.com/FrontisAI/OpenRSI.

Enterprise agents increasingly operate in workspaces, read heterogeneous files, use tools, and deliver business artifacts. We introduce EnterpriseClawBench, an internal enterprise agent benchmark constructed from proprietary agent sessions. Starting from a large archive of real workplace sessions, the EnterpriseClawBench pipeline produces 852 reproducible tasks with recovered fixtures, rewritten prompts, role classes, skill subclasses, hard rules, and semantic rubrics. Because the data contain internal enterprise content, the benchmark data are not released; the reusable contribution is the construction and evaluation protocol. On a manually audited 120-task Lite subset, the best score is only 0.663 for Codex with GPT-5.5. The results show that enterprise agent evaluation must report harness–model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than a single score.