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Computer Science > Artificial Intelligence

arXiv:2607.00454 (cs)

[Submitted on 1 Jul 2026]

Title:Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation

Authors:Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari

View a PDF of the paper titled Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation, by Vedant Balasubramaniam and Geetha Charan and Manojkumar Patil and Rohit P Suresh and V Priyanka and Kodur Sai Vinay Sathvik and Y. Narahari

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Abstract:Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties. Recent advisory systems powered by LLMs are liable for a different risk of generating recommendations that are agronomically credible but physiologically unconvincing. Agri-SAGE is a closed-loop framework designed to resolve the above two limitations by integrating retrieval-grounded multi-agent LLM reasoning with APSIM-based biophysical simulation, to generate and validate agronomic advisories. To assess this framework, we evaluate three reasoning approaches, namely Plan-and-Solve, Tree of Thoughts, and Reflexion, over a 10-year retrospective analysis. All three significantly outperform static PoP (Package-of-Practice) baselines, with Tree of Thoughts achieving impressive peak yields. At the same time, Reflexion achieves comparable agronomic outcomes at substantially lower computational cost by leveraging cross-seasonal episodic memory.

Subjects:Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as:arXiv:2607.00454 [cs.AI]
(or arXiv:2607.00454v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.00454

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arXiv-issued DOI via DataCite

Submission history

From: Geetha Charan [view email]

[v1] Wed, 1 Jul 2026 05:18:20 UTC (583 KB)

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