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[2602.00307] Autonomous Data Processing using Meta-Agents
Summary
The paper presents a novel framework, Autonomous Data Processing using Meta-Agents (ADP-MA), which enhances data processing pipelines through dynamic orchestration of agents for improved adaptability and optimization.
Why It Matters
As data processing needs evolve, traditional static pipelines become inadequate. ADP-MA addresses this gap by enabling autonomous monitoring and optimization, which is crucial for organizations seeking efficiency and scalability in data management.
Key Takeaways
- ADP-MA utilizes meta-agents to dynamically construct and optimize data processing pipelines.
- The framework emphasizes context-aware optimization and adaptive workload partitioning.
- It integrates a planning module, orchestration layer, and monitoring loop for iterative refinement.
- ADP-MA can reuse previously designed agents, reducing redundancy in pipeline construction.
- An interactive demo showcases the framework's capabilities in real-time data processing tasks.
Computer Science > Artificial Intelligence arXiv:2602.00307 (cs) [Submitted on 30 Jan 2026 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Autonomous Data Processing using Meta-Agents Authors:Udayan Khurana View a PDF of the paper titled Autonomous Data Processing using Meta-Agents, by Udayan Khurana View PDF HTML (experimental) 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 f...