In the evolving landscape of data science,
traditional monolithic automation tools often lack
flexibility, interpretability, and adaptability in complex,
real-world workflows. This paper proposes a novel multiagentic framework that leverages Artificial Intelligencedriven specialized agents to autonomously and
collaboratively manage the entire data science pipeline—
from preprocessing and exploratory data analysis to model
selection and performance evaluation. Each agent is
designed with domain-specific intelligence and operates
both independently and cooperatively within a decentralized
architecture, orchestrated via a lightweight communication
protocol. The framework promotes modularity, scalability,
and human-AI collaboration, while significantly reducing
manual intervention and operational overhead.
Experiments conducted on real-world datasets demonstrate
the system’s ability to deliver high-performing models with
improved efficiency and transparency. This approach not
only redefines AutoML by introducing agent-based
specialization but also sets the stage for the next generation
of AI-assisted data science platforms.