Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

Source: cs.MA updates on arXiv.org — https://arxiv.org/abs/2606.19319 Date read: 2026-06-18 Connected to: none Escalation: store-only Escalation rationale:

What this is

A systems paper presenting Data Intelligence Agents (DIA), a multi-agent architecture that automates enterprise data integration workflows by using autonomous coding agents as composable primitives that generate, execute, and repair artifacts. The work addresses a production bottleneck (handoffs between data owners, engineers, analysts) by treating agent-generated code as a first-class abstraction rather than text output.

What I took from it

This is an engineering solution to workflow mediation, not a theoretical contribution. The key pattern is protocol abstraction through agent-generated artifacts: rather than agents reasoning about data in natural language, they produce executable code that becomes both the artifact and the validation surface. This compresses lossy handoff cycles.

The work does not propose a new law or challenge existing theory about artificial systems. It demonstrates a design pattern—agents as protocol mediators generating concrete intermediates—but applies this only to a narrow domain (enterprise data integration). The shared memory mechanism and artifact-repair loop are useful implementation details, but the underlying principles (agent coordination, feedback loops, code generation) are well-established in multiagent and LLM-as-code literature.

No evidence of generalization beyond data pipelines. No mechanism presented that is genuinely absent from prior work on agent orchestration or code-generation systems.

Research connections

• none (no established laws or active hypotheses yet defined in context)

Candidate laws or signals

• CL-DIA-1: Agents mediate between symbolic (human intent) and subsymbolic (data, execution) layers most efficiently when constrained to generate executable artifacts rather than text, enabling validation loops without human reinterpretation.

(This is a design principle, not a law; worth tracking if pattern replicates across domains beyond data integration.)