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The article explores how enhanced reasoning in AI models can increase hallucination rates, proposing a 'Reasoning Tax' concept and emphasizing the need for robust context governance in enterprise applications.
This paper argues that agentic AI's next bottleneck is system scaling (designing the 'harness' around foundation models), not just model scaling, and introduces CheetahClaws, a Python-native reference harness, along with an analysis of three core bottlenecks: context governance, trustworthy memory, and dynamic skill routing.
This paper argues that advancing agentic AI requires scaling the system architecture around foundation models, focusing on auditable, modular, and verifiable components. The authors introduce CheetahClaws, a reference harness, and outline bottlenecks in context governance, trustworthy memory, and dynamic skill routing.