Enterprise AI agents keep failing because they forget what they learned

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 enough Enterprise 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 rel