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This paper proposes an organizational memory architecture for LLM-based agents to execute business processes using shared, organization-specific knowledge, addressing the scalability and consistency challenges of per-agent knowledge encoding.
Sentra CEO Ashwin Gopinath argues that memory for AI agents should be defined as retained consequence, where purpose determines which past information influences future behavior, distinguishing knowledge, context, and memory in enterprise settings.