LLM-based agents are evolving from single tools acting alone to complex multi-agent systems that collaborate, communicate, and adapt in real time.

Multi-agent systems unlock capabilities beyond any individual model:

→ Shared memory and communication
→ Specialized agent profiles
→ Real-time coordination in complex environments
→ Collective decision-making
→ Dynamic learning and adaptation

They’re already being applied to autonomous driving, software development, scientific research, and even large-scale world simulations from financial markets to disease modeling.

But challenges remain: How do we benchmark these systems? How do we ensure reliability at scale? And how do we go from coordination to true collective intelligence?

Based on the excellent survey, “Large Language Model based Multi-Agents: A Survey of Progress and Challenges,” our researcher Kevin Ros dives into how the next frontier of AI will evolve.