Enterprise AI adoption at scale requires more than large language models alone—it demands "agent logic," specialized software components that guide AI agents through complex, dynamic enterprise workflows while reducing costs and improving reliability. The article examines how agent logic, including knowledge graphs and program analysis tools, can steer AI models away from hallucinations and inefficiencies by constraining context to what's relevant for specific enterprise tasks. IBM's research demonstrates this approach across multiple domains, including mainframe application development, showing that intelligent guidance systems are critical for moving AI from failed pilots into core business operations.

IBM Research argues that scalable enterprise AI adoption requires more than large language models, pointing to "agent logic"—software primitives such as knowledge graphs, algorithms, and program analysis libraries that operate within an agent harness to steer LLMs toward enterprise workflows. The approach aims to reduce context space, lower token costs, and curb hallucinations while improving agent quality and end-user trust. IBM tested the concept by building agents for offerings including watsonx Code Assistant for Z, which accelerates mainframe application development and modernization.