In the first half of 2026, Databricks and MongoDB launched new AI context tools, kicking off a wave of vendor announcements aimed at improving AI agent performance. Without relevant context, AI agents struggle to provide accurate responses, making context retrieval a critical focus for data management vendors.
TL;DR
- Databricks and MongoDB launched new AI context tools in early 2026, sparking a wave of vendor announcements.
- AI agents need relevant context to provide accurate responses, making context retrieval a critical focus for data management vendors.
- Semantic modeling and vector search are key approaches to improving context retrieval for AI agents.
What happened
In January 2026, Databricks launched Instructed Retriever, a tool that improves on traditional retrieval-augmented generation (RAG) by adding parameters such as user instructions to searches to enhance retrieval accuracy. A week later, MongoDB unveiled new vector embedding and reranking models to improve the relevance of data discovered using vector search.
Following these launches, a wave of vendors, including Alteryx, AWS, Google Cloud, GoodData, Informatica, Snowflake, Tableau, Teradata, and ThoughtSpot, announced new tools aimed at improving AI agent performance by providing relevant context.
These tools focus on improving data discovery and retrieval, enabling customers to find and feed agents the context they need. Some vendors are simplifying and improving data retrieval systems, while others are enhancing vector search or adding semantic modeling capabilities.
Why it matters
AI agents need relevant context to provide accurate responses, making context retrieval a critical focus for data management vendors. Without relevant context, AI agents struggle to provide accurate responses, making context retrieval a critical focus for data management vendors.
The race to connect AI agents with relevant context is driven by the need to improve AI agent performance and trustworthiness in enterprise settings. As more organizations look to deploy AI agents, the ability to provide relevant context will be a key differentiator for data management vendors.
The emergence of semantic modeling as a key approach to improving context retrieval highlights the importance of data governance and security in the AI landscape. As AI agents become more prevalent, the need for robust data governance and security measures will only grow.
Key facts
- Databricks launched Instructed Retriever in January 2026, improving on traditional RAG by adding parameters such as user instructions to searches.
- MongoDB unveiled new vector embedding and reranking models in January 2026 to improve the relevance of data discovered using vector search.
- A wave of vendors, including Alteryx, AWS, Google Cloud, GoodData, Informatica, Snowflake, Tableau, Teradata, and ThoughtSpot, announced new tools aimed at improving AI agent performance by providing relevant context.
- Semantic modeling and vector search are key approaches to improving context retrieval for AI agents.
- Deloitte reported that one-quarter of organizations were able to move 40% of their AI projects into production in January 2026.
- Databricks' 2026 State of AI Agents report showed a 327% increase in the number of organizations transitioning from single chatbots to multi-agent systems over the previous four months.
- A group of data management and analytics vendors has banded together to create an open industry standard for semantic modeling.
- Blue Yonder uses semantic modeling provider AtScale's platform to rearchitect its data and connect it to AI tools.
Context
AI agents are becoming increasingly prevalent in enterprise settings, with organizations looking to deploy them for a variety of tasks. However, AI agents struggle to provide accurate responses without relevant context, making context retrieval a critical focus for data management vendors.
The race to connect AI agents with relevant context is driven by the need to improve AI agent performance and trustworthiness in enterprise settings. As more organizations look to deploy AI agents, the ability to provide relevant context will be a key differentiator for data management vendors.
The emergence of semantic modeling as a key approach to improving context retrieval highlights the importance of data governance and security in the AI landscape. As AI agents become more prevalent, the need for robust data governance and security measures will only grow.