A Composable Agentic System for Automated Visual Data Reporting
On the map: LLM agents and tool use → Multimodal and embodied agents
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/.
Key facts. Cited by 3 papers in the map (data through 7 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.
Builds on
3 of 17 references.
References · 3
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Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
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