An Agentic Orchestration System for Heliophysics Tasks

We propose an agentic orchestration system for heliophysics tasks. Heliophysics research faces significant challenges in synthesizing vast, heterogeneous datasets from multiple ground-based observatories and space missions, with traditional methodologies remaining largely manual and siloed. This paper presents an agentic orchestration system that addresses these limitations by enabling integration and interaction between computational models across heliophysics research. The system employs Large Language Model-based agents structured according to established design patterns. Our implementation leverages state-of-the-art orchestration primitives, specifically Anthropic's Model Context Protocol for tool description and Google's Agent Development Kit for agent-to-agent communication. The system incorporates domain-specific tools ranging from ionospheric models to solar surface simulations, augmented by Retrieval Augmented Generation containing heliophysics literature and worked examples. valuation was conducted through demonstrated capabilities in ionospheric modeling, solar surface analysis, automated pipeline generation, and tool discovery. Our system autonomously generates data pipelines, creating and managing computational infrastructure, all whilst requiring human oversight for critical decisions. The system reduces prototyping time from months to minutes, providing natural language access to sophisticated heliophysics simulations and machine learning models. This work establishes a first-attempt for accelerated scientific discovery in heliophysics by improving access to computational tools and enabling rapid hypothesis testing through automated workflow orchestration.