Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic

Nicholas Fuller of IBM Research argues that scalable enterprise AI adoption requires agent logic—software primitives like knowledge graphs and program analysis libraries that steer LLMs within enterprise workflows. Writing on Hugging Face, Fuller details IBM tools including watsonx Code Assistant for Z and Aster, which achieved up to 30× lower token consumption and 20–45% improved test coverage compared to baseline LLM-only approaches across multiple mission-critical applications.