We Killed RAG, MCP, and Agentic Loops. Here's What Happened (www.decodingai.com)

In a recent presentation at the Antler startup incubator, ZTRON CEO Stephen Ribbon and his team shared their journey of building a vertical AI agent for financial advisors, highlighting key lessons from their experience. They revealed their decision to limit the use of Retrieval-Augmented Generation (RAG) and the Multimodal Cognitive Protocol (MCP), which they found led to unnecessary complexity and performance issues. Initially, their approach relied on an intricate multi-index RAG system and agentic RAG loops for complex queries, but they observed that this not only slowed down response times significantly but also created instability in their application. The significance of ZTRON's findings lies in their practical insights for the AI/ML community, illustrating the importance of simplicity in AI architectures. By pivoting away from over-engineered solutions, they adopted fundamental principles to deliver a more stable product. Utilizing tools like DBOS for durable workflows and decoupling their ingestion process allowed them to scale efficiently while maintaining performance. Ultimately, ZTRON's experience serves as a valuable case study for startups navigating the rapidly evolving AI landscape, emphasizing the need to focus on what truly solves user problems without getting lost in complex technologies.