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

---
type: paper
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
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2607.00454
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-07-01
updated_at: 2026-07-01
status: queued
relevance: high
topics:
  - agent-evaluation
  - agent-safety
  - computer-use
  - memory
  - multi-agent
  - planning
  - rag
  - reasoning
  - world-model
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cs.AI
  - cs.MA
related_concepts:
  -
related_jobs:
  -
related_experiments:
  -
related_projects:
  -
collection_score: 20
collection_queries: multi-agent-llm
---

## One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

## Why Collected

- matched queries: multi-agent-llm
- inferred topics: agent-evaluation, agent-safety, computer-use, memory, multi-agent, planning, rag, reasoning, world-model
- arXiv categories: cs.AI, cs.MA
- collection score: 20

## Review Checklist

- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?

## Links

- arXiv: https://arxiv.org/abs/2607.00454