# Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking arXiv: https://arxiv.org/abs/2411.10184 ยท HTML: https://mlsanity.com/paper/2411.10184/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 15 of its 87 references that are in the map. - First posted on arXiv 15 Nov 2024. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 15 of 15) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023) - [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022) - [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023) - [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020) - [Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge](https://mlsanity.com/paper/2102.03315/) (2021) - [Large Language Models for Supply Chain Optimization](https://mlsanity.com/paper/2307.03875/) (2023) - [Welfare Diplomacy: Benchmarking Language Model Cooperation](https://mlsanity.com/paper/2310.08901/) (2023) - [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023) - [Self-Reflection in LLM Agents: Effects on Problem-Solving Performance](https://mlsanity.com/paper/2405.06682/) (2024) - [InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains](https://mlsanity.com/paper/2407.11384/) (2024) - [Pre-Trained Large Language Models for Industrial Control](https://mlsanity.com/paper/2308.03028/) (2023) - [A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management](https://mlsanity.com/paper/2306.07542/) (2023) ## Built on by (most cited first, 16 of 16) - [Virtual Agent Economies](https://mlsanity.com/paper/2509.10147/) (2025) - [Rethinking Supply Chain Planning: A Generative Paradigm](https://mlsanity.com/paper/2509.03811/) (2025) - [Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management](https://mlsanity.com/paper/2605.17036/) (2026) - [Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks](https://mlsanity.com/paper/2510.06307/) (2025) - [Automating Supply Chain Disruption Monitoring via an Agentic AI Approach](https://mlsanity.com/paper/2601.09680/) (2026) - [Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains](https://mlsanity.com/paper/2604.05987/) (2026) - [Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections](https://mlsanity.com/paper/2603.18914/) (2026) - [Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs](https://mlsanity.com/paper/2605.26835/) (2026) - [Optimizing delivery for quick commerce factoring qualitative assessment of generated routes](https://mlsanity.com/paper/2510.08671/) (2025) - [AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval](https://mlsanity.com/paper/2602.05524/) (2026) - [Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation](https://mlsanity.com/paper/2604.17220/) (2026) - [Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing](https://mlsanity.com/paper/2606.31820/) (2026) - [Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems](https://mlsanity.com/paper/2607.25405/) (2026) - [SynChain: Inducing Computer-Use Agent Systems to Construct Their Own Attack Chains](https://mlsanity.com/paper/2608.06862/) (2026) - [A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing](https://mlsanity.com/paper/2608.07148/) (2026) - [Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains](https://mlsanity.com/paper/2609.31282/) (2026) ## Abstract (arXiv) This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels and delivery times require coordination among companies. Traditional SCM relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect. Some routine consensus processes, especially those that are time-intensive and costly, can be automated. Existing solutions for automated coordination have faced challenges due to high entry barriers locking out SMEs, limited capabilities, and limited adaptability in complex scenarios. However, recent advances in Generative AI, particularly LLMs, show promise in overcoming these barriers. LLMs, trained on vast datasets can negotiate, reason, and plan, facilitating near-human-level consensus at scale with minimal entry barriers. In this work, we identify key limitations in existing approaches and propose autonomous LLM agents to address these gaps. We introduce a series of novel, supply chain-specific consensus-seeking frameworks tailored for LLM agents and validate the effectiveness of our approach through a case study in inventory management. To accelerate progress within the SCM community, we open-source our code, providing a foundation for further advancements in LLM-powered autonomous supply chain solutions.