TL;DR
ADP-MA (Autonomous Data Processing using Meta-Agents), a system that autonomously solves a complex and diverse set of data processing tasks, outperforming published single-agent baselines.
Comparisons against stronger model and coding-agent competitors further indicate that both domain-specific agent runtime structure and foundation-model strength matter for autonomous data analysis.
MUSE is presented, an interactive meta-agent that enhances user understanding and control of agentic data science systems by dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
It is argued that verification, not generation, is the binding constraint for trustworthy automated analysis in agentic data science: systems in which an LLM coordinates exploratory analysis, query generation, hypothesis formation, and reporting with limited human supervision.
Two methodology contributions are presented: a declarative-markdown harness with a small mutation surface, and an auto-research loop in which failure-mode analysis on completed experiments proposes new hypotheses, optionally human-reviewed, that progress through the same experiment workflow.
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