Beyond Single Prompts: Building Adaptive Multi-Agent Simulations with LangChain

Most LLM applications focus on single-turn interactions, but real-world systems require agents to navigate incomplete information, adapt strategies, and coordinate under uncertainty. This project demonstrates how autonomous LLMs behave when embedded in complex social dynamics, offering insights into emergent agent strategies and collective decision-making. For AI developers, it bridges the gap between isolated prompt-response patterns and genuinely adaptive multi-agent systems.