An Agentic Orchestration System for Heliophysics Tasks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".