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@dair_ai

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own the harness, and that is increasingly where agent quality is won or lost. The default mental model still puts all the weight on the foundation model. Bigger model, better agent. But agent behavior actually emerges from the whole stack around it. Memory substrate, context constructor, skill routing, orchestration loop, and the verification and governance layer. This new research calls that stack the harness and argues we should treat it as a first-class object of design and evaluation. It names three core bottlenecks to scale. Context governance, trustworthy memory, and dynamic skill routing. It also ships CheetahClaws, a Python-native reference harness, and compares it with Claude Code and OpenClaw. Paper: https://t.co/HynpWFVUqq Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

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