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Databricks Introduces Agent Bricks to Build Production-Ready AI Agents on Enterprise Data
- Databricks introduced Agent Bricks at the Data + AI Summit for building and deploying AI agents using enterprise data.
- Agent Bricks automates AI agent development by transforming task descriptions and data into deployable agents.
- The system uses synthetic data and benchmarks to optimize agent performance based on accuracy and cost tradeoffs.
- Use cases include Information Extraction Agent, Knowledge Assistant Agent, Multi-Agent Supervisor, and Custom LLM Agent.
- Agent Bricks addresses challenges in AI development by providing domain-specific evaluations and a streamlined workflow.
- AstraZeneca and Flo Health are early adopters seeing positive results with Agent Bricks in data extraction and accuracy improvements.
- Databricks also launched serverless GPU support and MLflow 3.0 to enhance AI system management.
- MLflow 3.0 includes features for generative AI like prompt management and integration with data lakehouses.
- The combination of Agent Bricks, serverless GPUs, and MLflow 3.0 positions Databricks as a comprehensive platform for enterprise AI systems.
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