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

This post details how agent logic, implemented through software primitives like knowledge graphs and program analysis libraries, can steer LLMs within an agent harness to achieve more performant and cost-effective enterprise AI adoption. It presents four key domains where agent logic is applied: understanding legacy code (Cobol/PL/1) using program analysis and static analysis for IBM watsonx Code Assistant for Z; expediting test generation with the Aster library for Java applications; proactively responding to incidents and enabling shift-left app resiliency through observability-driven orchestration with knowledge graphs and program analysis; and automating IT compliance modernization using algorithms and adaptive planning. Case studies in healthcare (Configurable Generalist Agent - CUGA) and condition-based maintenance for physical assets further illustrate these principles.