Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
arXiv:2606.19319v1 Announce Type: cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Query Generator) that compresses this workflow by treating autonomous coding agents (ACAs) as a first-class abstraction: rather than emitting text, the agents generate, execute, validate, and repair concrete artifacts, draw on a shared memory for experi
The development of sophisticated large language models and autonomous agents has reached a point where applying them to complex enterprise data workflows is becoming feasible.
This development proposes a significant leap in automating data integration and querying, removing human bottlenecks and increasing efficiency in data-driven decision-making.
Enterprise data workflows can shift from being human-centric and lossy to being agent-driven, automated, and more robust through concrete artifact generation and validation.
- · Enterprise data teams
- · Businesses with complex data landscapes
- · AI software providers
- · Data scientists
- · Manual data integration consultants
- · Inefficient legacy data systems
- · Entry-level data engineering tasks
Enterprise data integration becomes significantly faster and less error-prone, accelerating time-to-insight.
Reduced operational costs for data management and an increased demand for AI-driven data tools and governance solutions.
Competitive advantage shifts to companies best able to leverage autonomous agents for real-time data intelligence across their operations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI