arXiv:2606.00708v1 Announce Type: cross Abstract: Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation protocol, and refinement strategy for a task. AutoML systems automate parts of this process, but typically search within predefined pipeline, model, and hyperparameter spaces. LLM-based agents offer greater flexibility through retrieval, code generation, and execution feedback, yet their modelling decisions are often unstructured, difficult to verify, and hard to reuse
The paper addresses the current limitations of LLM-based agents in structured decision-making for complex tasks like automated data science, suggesting a more modular and verifiable approach.
This work introduces a framework for more reliable and reusable AI agents, which is critical for their widespread adoption in complex and high-stakes white-collar workflows.
The focus shifts from unstructured LLM-based agentic decision-making to a more systematic, verifiable, and composable orchestration of AI agents, potentially accelerating their practical application.
- · AI developers
- · Data science platforms
- · Enterprise software companies
- · Automation solution providers
- · Manual data scientists (routine tasks)
- · Unstructured AI agent frameworks
- · Companies reliant on bespoke AI solutions
More robust and scalable AI agents will reduce the cost and increase the efficiency of complex data science and white-collar tasks.
The modular and verifiable nature of MOSAIC could enable a marketplace for agentic components, fostering specialization and innovation.
This could lead to a significant acceleration in the autonomous automation of entire business processes, transforming enterprise structures.
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Read at arXiv cs.LG