AI agents are becoming increasingly capable of handling research, software development, customer communication and repetitive business operations. However, deploying several agents across different applications can create a new problem: each agent may have its own context, tools, permissions and working environment. Kylon is addressing this challenge with a platform it calls a company harness—a shared […]
Meta AI and the University of Illinois have published EvoHarness-RL, a framework that trains an agent to run its own memory, progress and experience stores instead of following a hand-written harness. On the ALFWorld benchmark it took Alibaba’s open-weight Qwen3-8B from 47.9% to 96.9%, a whisker past Claude Opus 4.5’s unaided 96.4%. This breakdown covers what the framework actually trains, the full results table rather than the two rows that travel well, why the same harness pushes Opus 4.5 to 98.5%, why Claude Opus sits inside the training loop as teacher and consolidator, what harness annealing and harness evolution mean for latency, and what any of it is worth if you are shipping agents commercially.
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