Most enterprise AI pilots aren't failing because the model is too weak. They're failing because the model has no idea where it is. IBM Research dropped a post this week making the case that the missing layer isn't a better LLM — it's agent logic: domain-specific software primitives that give the model a map before it starts driving.
"Agent logic is software primitives, such as knowledge graphs, algorithms, program analysis libraries, which operate at the agentic layer (within an agent harness) and can intentionally steer the LLM in the direction of the enterprise workflow, reducing the context space."
What IBM actually built
Four production use cases, four sets of hard numbers:
Legacy code understanding (COBOL/PL1): ~30× lower token consumption vs. baseline LLM-only approach, while maintaining performance on up to 1M lines of code. Program analysis libraries chunked the problem; the LLM only touched what mattered.
