Semi-Autonomous AI Pipeline Brings Transparency to Exploratory Data Analysis

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Developers at DEV Community have detailed a four-stage agentic EDA pipeline that uses multiple LLM agents to analyze datasets in a structured, semi-autonomous workflow. Unlike a single LLM call, the system breaks exploratory data analysis into fixed stages covering data cleaning, univariate charting, relationship detection, and report writing. Each stage uses a plan-then-act model, where the LLM handles judgment calls such as chart selection, while all calculations and code execution are handled by deterministic code. The pipeline sits deliberately between fully hardcoded and fully autonomous systems, giving the model real decision-making power only within predefined checkpoints. This design aims to make AI-driven data analysis more auditable and reliable, avoiding the opaque outputs common to single-prompt approaches.

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