Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic
Hugging Face Blog
IBM implemented agent logic—software primitives like knowledge graphs and program analysis libraries—to guide large language models through enterprise workflows in four domains: legacy code understanding, test generation, incident response, and compliance automation. The approach achieved up to 30× lower token consumption in legacy code analysis, 20-45% improvements in test coverage with 15× fewer tokens, 4.0× better incident investigation performance, and 1.3-2.0× better compliance outcomes compared to baseline LLM-only or competing agent approaches. By constraining LLM reasoning through structured task decomposition and domain-specific logic, enterprises can reduce costs while improving accuracy and adoption of AI systems in mission-critical workflows.
