Researchers from Meta AI and the University of Illinois Urbana-Champaign introduced EvoHarness-RL, a trainable coordination layer that enables large language model agents to create and manage their ow…
RL environments are the essential data for training AI agents, according to a recent perspective shared by an industry commentator. The author argues that just as curated datasets enabled deep learnin…
Lilian Weng published a survey on harness engineering for self-improving agents, documenting how agents that optimize their own scaffolding face provenance and trust issues. The Darwin Gödel Machine p…
LangChain co-founder Harrison Chase argues that AI agents can learn at three distinct layers—model, harness, and context—but most teams focus only on retraining the model. The harness layer, which opt…
Harness Forge, a Claude Code skill, reimplements the Meta-Harness optimization loop natively, reducing code from ~1,260 lines to ~75 lines by leveraging Claude Code's built-in agent runtime. The skill…