# A Composable Agentic System for Automated Visual Data Reporting

arXiv: https://arxiv.org/abs/2509.05721 · HTML: https://mlsanity.com/paper/2509.05721/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 3 of 3)

- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [DracoGPT: Extracting Visualization Design Preferences from Large Language Models](https://mlsanity.com/paper/2408.06845/) (2024)
- [Draco 2: An Extensible Platform to Model Visualization Design](https://mlsanity.com/paper/2308.14247/) (2023)

## Built on by (most cited first, 3 of 3)

- [TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization](https://mlsanity.com/paper/2603.25063/) (2026)
- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)
- [Plainbook: Data Science, in Plain Language](https://mlsanity.com/paper/2607.05717/) (2026)

## Abstract (arXiv)

To address the brittleness of monolithic AI agents, our prototype for automated visual data reporting explores a Human-AI Partnership model. Its hybrid, multi-agent architecture strategically externalizes logic from LLMs to deterministic modules, leveraging the rule-based system Draco for principled visualization design. The system delivers a dual-output: an interactive Observable report with Mosaic for reader exploration, and executable Marimo notebooks for deep, analyst-facing traceability. This granular architecture yields a fully automatic yet auditable and steerable system, charting a path toward a more synergistic partnership between human experts and AI. For reproducibility, our implementation and examples are available at https://peter-gy.github.io/VISxGenAI-2025/.