Enterprise AI agents keep failing because they forget what they learned
2026-05-21
Summary
We introduce the 'Decision Context Graph' framework that helps enterprise AI agents go beyond simple information retrieval (RAG) and perform complex decisions.
Key Points
- Existing RAG architectures are useful for document retrieval, but they are a major cause of agent failure in enterprise environments due to their inability to determine applicability across contexts, rule conflicts, and information validity over time.
- The decision context graph proposed by Rippletide structures the time dimension and explicit logic, allowing agents to reason based on previous findings without forgetting what they have learned.
- This framework reduces errors in multi-step tasks by allowing agents to clearly determine which rules and exceptions are applicable in a particular situation, rather than relying on probabilistic guesses.
Notable Quotes & Details
Notable Data / Quotes
- "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?" - Yann Bilien (Rippletide co-founder)
- “The biggest thing builders struggle with is the gap between retrieval and applicability.” - Wyatt Mayham (Northwest AI Consulting)
Intended Audience
Enterprise AI agent developers and architects