Autonomous Data Processing using Meta-Agents
🤖 AI Summary
This work proposes a hierarchical meta-agent architecture to overcome the static and rigid nature of traditional data processing pipelines, which struggle to autonomously monitor and optimize themselves post-deployment. The framework integrates three core components—planning, agent orchestration, and a monitoring feedback loop—to enable dynamic construction, execution, and iterative refinement of end-to-end workflows. It introduces context-aware optimization, adaptive workload partitioning, and progressive sampling mechanisms, facilitating agent reuse and seamless integration with external tools. These innovations significantly enhance system flexibility and scalability. Experimental results demonstrate the framework’s effectiveness and practicality in automatically constructing, continuously monitoring, and adaptively optimizing diverse data processing tasks.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
autonomous data processing
meta-agents
adaptive pipeline optimization
end-to-end pipeline management
context-aware data processing
Innovation
Methods, ideas, or system contributions that make the work stand out.
meta-agents
autonomous data processing
hierarchical agent orchestration
adaptive pipeline optimization
iterative refinement
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