RAG architectures are good at one thing: surfacing semantically relevant documents. That’s also where they stop.A framework called a decision context graph addresses that gap by giving agents structured memory, time-aware reasoning, and explicit decision logic. Rippletide, a startup in the Neo4j ecosystem, has built one. The key capability: agents that are non-regressive, able to freeze validated sequences of actions and compound on them over time.“The key point you want is non-regressivity: How do you make sure that, when the agent will generate something new, you can compound on the previous discoveries?” said Yann Bilien, Rippletid’s co-founder and chief scientific officer. Why RAG doesn’t go far enoughEnterprise context is sprawled across ERP tools, logs, databases, vector stores, and policy documents. Generative AI tools can retrieve from all of it — through keyword search, SQL queries, or full RAG pipelines — but retrieval has a ceiling.Notably, data retrieved may not be relevant to the decision at hand (thus causing hallucinations); and, even if agents do pull the right data, they often lack guidance to make decisions backed by a strong rationale. That is, RAG retrieves documents, not decision context. “Everyone starts with RAG: Pull relevant docs, stuff them in the prompt, let the model figure it out,” said Wyatt Mayham of Northwest AI Consulting. While that works fine for chatbots, it “breaks immediately” for agents that need to make decisions and take actions, he pointed out. “The biggest thing builders struggle with is the gap between retrieval and applicability.” A retrieved document doesn’t tell the agent whether it still applies, whether it’s been superseded, or whether there’s a conflicting rule that takes priority, Mayham said. “Ag …
