Supply chain management is a constant juggling act, requiring countless decisions about inventory, deliveries, and everything in between. A major challenge? Getting everyone on the same page. Traditionally, coordinating these moving parts has relied heavily on human negotiation, a process that’s both time-consuming and prone to inefficiencies like the bullwhip effect (where small changes in demand snowball into huge production swings). While automated systems have been proposed, they’ve struggled to gain traction due to high implementation costs and difficulty adapting to complex real-world scenarios. However, the rise of large language models (LLMs) like Gemini offers a new path forward. LLMs, trained on vast amounts of data, can reason, plan, and even negotiate, potentially automating these crucial consensus-seeking processes. Researchers are exploring how LLM-powered agents can streamline supply chain management. Imagine AI agents representing different companies, negotiating order quantities and delivery schedules in real-time. This research explores different LLM-powered consensus-seeking frameworks, from simple information sharing to more sophisticated negotiation tactics, and evaluates their impact on critical metrics like overall costs and the bullwhip effect. Early results are promising, suggesting that LLM agents can indeed reduce costs and mitigate the bullwhip effect compared to traditional methods. Interestingly, the research found that simply plugging in an algorithm isn’t enough. LLM agents, much like their human counterparts, need guidance on how to effectively use these tools. The most effective frameworks incorporated negotiation and information exchange between agents, allowing them to learn from each other and reach more balanced solutions. While fully autonomous supply chains are still a way off, LLMs are showing real potential to revolutionize how we manage these complex systems. As the technology matures and our understanding of LLM behavior deepens, we can expect even more sophisticated and reliable AI-driven solutions for supply chain optimization. This research opens the door to a future where AI-powered negotiation helps businesses make smarter, faster decisions, leading to more resilient and efficient supply chains for everyone.