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

arXiv: https://arxiv.org/abs/2607.00454 · HTML: https://mlsanity.com/paper/2607.00454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 17 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 15 of 15)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging](https://mlsanity.com/paper/2502.05664/) (2025)
- [Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework](https://mlsanity.com/paper/2411.16707/) (2024)
- [LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins](https://mlsanity.com/paper/2405.18092/) (2024)
- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers](https://mlsanity.com/paper/2409.08916/) (2024)
- [AgriGPT: a Large Language Model Ecosystem for Agriculture](https://mlsanity.com/paper/2508.08632/) (2025)
- [ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources](https://mlsanity.com/paper/2409.13537/) (2024)
- [GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture](https://mlsanity.com/paper/2403.11858/) (2024)
- [Large language models can help boost food production, but be mindful of their risks](https://mlsanity.com/paper/2403.15475/) (2024)
- [AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice](https://mlsanity.com/paper/2512.10114/) (2025)
- [AgroAskAI: A Multi-Agentic AI Framework for Supporting Smallholder Farmers' Enquiries Globally](https://mlsanity.com/paper/2512.14910/) (2025)
- [Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory](https://mlsanity.com/paper/2603.03294/) (2026)

## Abstract (arXiv)

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.