Apple study finds minimal agent matches or beats multi-agent ML engineering systems

The AI industry has spent a lot of energy building teams of agents that plan, delegate, critique and coordinate. A new Apple study suggests that much of that machinery may not be doing much work. Researchers at Apple Machine Learning Research found that one well-prompted coding agent, given basic shell access, matched or outperformed several complex multi-agent systems on automated machine learning engineering tasks. What Apple tested The paper is titled “How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?” It was submitted to arXiv on September 30, 2026, and drew attention in early October 2026. The research team includes Alejandro Hernández-Cano, Kirill Brilliantov and Emmanuel Abbé. Their question was simple: how much scaffolding does a capable model actually need? A “harness” is the software wrapped around a large language model that lets it act: tools, memory, planning loops and, in fancier setups, multiple cooperating agents. That stripped-down agent is called Malena. It runs in a single session and can do three things: read files, write files and run bash commands. The researchers pitted Malena against four more elaborate systems: MLEvolve, AiScient...