IBM Research introduces 'agent logic'—software primitives like knowledge graphs and program analysis that guide LLMs to focus on enterprise workflow cores, dramatically reducing token consumption and improving performance. Validated across legacy code understanding, test generation, incident response, and compliance automation, agent logic enables AI agents to outperform pure LLM approaches with up to 30x token reduction in app understanding and 20-45% coverage improvement in test generation. Case studies include a configurable generalist agent (CUGA) in healthcare and condition-based maintenance for IBM Global Real Estate.

The article discusses the rapid growth of the AI economy (US AI GDP growing ~2600% per year), challenges of AI oversight using AI, the GPIC dataset of 100M licensed images, and the protein folding model ESMFold2 for cancer research.

This article explores the critical role of agent logic—such as knowledge graphs and program analysis libraries—in enabling scalable enterprise AI adoption. Through IBM's practices in four domains (legacy code understanding, test generation, incident response, compliance modernization), it shows how agent logic reduces LLM context, lowers costs, and improves performance.