Stanford Research: Limitations of LLMs Can Be Overcome by Carefully Designed Multi-Agent Collaboration

Most AI tools today rely on single large language models (LLMs) that hallucinate, forget constraints, and can't validate their own outputs. This is a known limitation in complex planning tasks like supply chain management.

City AI helped translate their academic research—MACI (Multi-Agent Collaborative Intelligence)—into the backbone of City AI. MACI replaces brittle single-agent reasoning with a structured system of agents: planners, validators, monitors, and constraint enforcers.

City AI is the first commercial system to operationalize MACI in global logistics—forecasting risks, rerouting in real-time, and coordinating agents like a team of human analysts. From a Thanksgiving dinner case study to real-world trade routes, the framework has shown measurable gains in both accuracy and adaptability.

We turned the research into a product layer: clear UI, explainable outputs, and agent interoperability that brings AI from pattern-matching to decision-making.

We turned the research into a product layer: clear UI, explainable outputs, and agent interoperability that brings AI from pattern-matching to decision-making.