Enterprise data is rarely useful in a silo. Answering questions like, "Which of our products have had declining sales over the past three months, and what potentially related issues are brought up in customer reviews on various seller sites?" requires reasoning across a mix of structured and unstructured data sources, including data lakes, review data, and product information management systems. In this blog, we demonstrate how Databricks Agent Bricks Supervisor Agent (SA) can help with these complex, realistic tasks through multi-step reasoning grounded in a hybrid of structured and unstructured data.

With tuned instructions and careful tool configuration, we find SA to be highly performant on a wide range of knowledge-intensive enterprise tasks. Figure 1 shows that SA achieves 20% or more improvement over SoTA baselines on:

Supervisor Agent demonstrates significant gains on a wide range of economically valuable tasks: from academic retrieval (+21% on STaRK-MAG) to biomedical reasoning (+38% on STaRK Prime) and financial analysis (+23% on FinanceBench).

Agent Bricks Supervisor Agent is a declarative agent builder that orchestrates agents and tools. It is built on aroll — an internal agentic framework for building, evaluating, and deploying multi-step LLM workflows at scale.1 aroll and SA were specifically designed for the advanced agentic use cases our customers frequently encounter.

aroll enables adding new tools and custom instructions through simple configuration changes, can handle thousands of concurrent conversations and parallel tool executions, and incorporates advanced agent orchestration and context management techniques to refine queries and recover from partial answers. All of these are difficult to achieve with SoTA single-turn systems today.

Because SA is built on this flexible architecture, its quality can be continually improved through simple user curation, such as tweaking top-level instructions or refining agent descriptions, without needing to write any custom code.