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

Scalable enterprise AI adoption requires more than just large language models; it depends on agent logic. Agent logic consists of software primitives like knowledge graphs and program analysis libraries that steer an LLM, reducing the context space for more efficient and performant outcomes. This approach makes AI agents more cost-effective and trustworthy for dynamic, long-running enterprise workflows. Specific IBM use cases, such as modernizing legacy code and automating test generation, demonstrate that agents equipped with this logic achieve superior results with significantly lower token consumption compared to using a frontier LLM alone.