TL;DR
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.
Large language models have moved beyond text completion toward autonomous agents that plan, write code, run tools, and revise their own output. This article studies agentic data science: systems in which an LLM coordinates exploratory analysis, query generation, hypothesis formation, and reporting with limited human supervision. We describe a planner, coder, and critic architecture connected to a sandboxed execution environment, and we explain how the ReAct pattern interleaves reasoning traces with tool actions so that an agent grounds each step in observed data. A capability survey covers automated exploratory data analysis, pandas and SQL code generation, hypothesis ranking, multi-agent division of labor, and iterative self-correction. Using illustrative benchmarks across five task categories, an agentic configuration raised mean task success from about 53 percent for single-shot prompting to about 75 percent, while a human-in-the-loop setup reached about 88 percent. Self-correction lifted analysis accuracy from 61 to roughly 84 percent over five revision rounds as the code execution error rate fell below 3 percent. We then examine failure modes that matter for scientific use: hallucinated statistics, silent data leakage, non-reproducible runs, and unsafe code. Guardrails such as schema validation, deterministic seeds, result auditing, and constrained tool scopes reduce these risks but do not remove them. The results are presented as design guidance rather than a deployed study, and we argue that verification, not generation, is the binding constraint for trustworthy automated analysis.
SyntheticAgentTraceQA is proposed, an execution- first framework for generating scalable supervision data for tool- augmented agents and shows that execution-grounded supervision improves tool execution behavior, reference-trace agreement, and answer-generation performance on the evaluated tasks.
Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly out-performing previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE’s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials.
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language. Although these systems can significantly reduce manual effort, it remains difficult to diagnose their behavior and steer the reasoning process when failures or unexpected outputs occur. We present MUSE, an interactive meta-agent that enhances user understanding and control of agentic data science systems by (1) dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details; (2) enabling users to reference specific workflow steps in context to ask grounded questions, provide feedback, and revise problematic steps without manually locating relevant execution history; and (3) supporting mixed-initiative steering by surfacing suspicious steps for inspection, scaffolding the repair process, and translating user repair intent into contextualized instructions for the underlying agent. In a between-subjects study (n = 15), MUSE improved task efficiency and increased users'confidence in understanding and steering agentic data science workflows.