Adrian Rebmann

The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.

The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.