Beyond RAG: Agentic Workflows Require Knowledge Systems That Execute, Not Just Retrieve

An Equaldocs analysis published July 10 documented the structural shift from RAG-based legal AI to agentic workflows — framing it as the difference between a tool that "merely summarizes text" and a workflow that "actively executes business operations." The technical contrast is precise: basic RAG answers one question per interaction by retrieving and synthesizing relevant documents; agentic workflows analyze matters, draft multiple documents, update administrative records, and sync with productivity tools (Word, Outlook) in a multi-step sequence without human intervention between every step. The Smokeball Archie upgrade — from RAG-style assistant to multi-step agentic orchestrator across matter, document, and workflow — is cited as a production example. The KM architecture implication is fundamental: RAG systems require a retrievable knowledge corpus; agentic systems require knowledge that is structured for workflow execution — tagged by practice area, matter type, client position, and output stage, not just full-text searchable. Law firm KM directors who have built their AI readiness around a document retrieval model need to assess whether their knowledge structures support sequential, multi-step agent execution — a materially different architectural requirement.