Databricks has launched Agent Bricks to help customers build Data Intelligent Agents. The platform focuses on the most critical AI use cases that companies are currently struggling with.
Databricks recognizes that developing AI remains a fragmented process for many organizations. Teams waste a lot of time connecting separate tools and systems. They struggle with the complexity of GPUs and spend hours manually tuning models.
The challenges go beyond technical complexity. Many models fail to properly understand domain-specific data. Once the agents are built, developers have no choice but to use trial and error to address quality and effectiveness. This makes it difficult for companies to implement production-ready AI that delivers real ROI.
Agent Bricks as a solution
Agent Bricks addresses these pain points by focusing on the most important AI use cases for businesses. The platform can intelligently select the right AI model and storage configuration for any AI situation. According to Databricks, it generates much higher output quality than existing competing solutions.
A key advantage of Agent Bricks is its ability to generate synthetic data. This helps fill the data scarcity gap that previously prevented developers from creating large, diverse datasets for model training.
Databricks has previously worked on strengthening Mosaic AI as a foundation for AI apps. At that time, the platform introduced tools for model quality and AI governance. The new Agent Bricks builds on these developments.