Throughout the first six months of 2026, data management vendors unveiled and launched new capabilities designed to connect AI agents with the context needed to perform in production. Just days into January, Databricks and MongoDB each introduced new capabilities aimed at discovering and delivering agents the relevant data and business logic they require. Without appropriate context, AI agents will fail, as Michael Bendersky, director of research at Databricks, stated: "Without it, they're guessing." Connecting agents with relevant context is complex, as data and business logic need to be prepared for AI, a lengthy process for organizations with data spread across disparate systems. As a result, with most AI projects failing heading into 2026, all other data management trends have receded. David Menninger, an analyst at ISG Software Research, noted that without context, an agent is at risk of taking the wrong action. To improve data discovery and retrieval, Databricks launched Instructed Retriever, which improved on traditional retrieval-augmented generation by adding parameters such as user instructions to searches. MongoDB unveiled new vector embedding and reranking models to improve the relevance of data discovered using vector search. Other vendors such as Alteryx, AWS, Google Cloud, GoodData, Informatica, and Snow also made announcements.