Autonomous Data Processing using Meta-Agents

TL;DR

This paper introduces ADP-MA, a hierarchical meta-agent framework that autonomously constructs, manages, and refines data processing pipelines, enhancing adaptability and efficiency over traditional static pipelines.

Contribution

The paper presents a novel hierarchical meta-agent architecture for autonomous, adaptive data pipeline management, integrating planning, orchestration, and iterative evaluation.

Findings

Demonstrates dynamic pipeline construction and refinement.

Shows improved scalability and adaptability.

Reduces redundancy through agent reuse.

Abstract

Traditional data processing pipelines are typically static and handcrafted for specific tasks, limiting their adaptability to evolving requirements. While general-purpose agents and coding assistants can generate code for well-understood data pipelines, they lack the ability to autonomously monitor, manage, and optimize an end-to-end pipeline once deployed. We present \textbf{Autonomous Data Processing using Meta-agents} (ADP-MA), a framework that dynamically constructs, executes, and iteratively refines data processing pipelines through hierarchical agent orchestration. At its core, \textit{meta-agents} analyze input data and task specifications to design a multi-phase plan, instantiate specialized \textit{ground-level agents}, and continuously evaluate pipeline performance. The architecture comprises three key components: a planning module for strategy generation, an orchestration layer for agent coordination and tool integration, and a monitoring loop for iterative evaluation and backtracking. Unlike conventional approaches, ADP-MA emphasizes context-aware optimization, adaptive workload partitioning, and progressive sampling for scalability. Additionally, the framework leverages a diverse set of external tools and can reuse previously designed agents, reducing redundancy and accelerating pipeline construction. We demonstrate ADP-MA through an interactive demo that showcases pipeline construction, execution monitoring, and adaptive refinement across representative data processing tasks.