AI agents based on LLMs or VLMs have already demonstrated their exceptional ability to solve complex problems, and increasingly, these models are being extended to a wide range of downstream applications, such as workflow automation on operating systems, scientific research and discovery, and embodied AI. The integration of foundation models like VLM, VLA, and generative models, combined with external scaffolds like memory mechanisms, system prompts, external knowledge bases, and toolkits, has enabled the emergence of systematic agents capable of tackling complex, long-sequence tasks. However, human society is a complex system formed by diverse organizations, where multiple individuals collaborate and compete within a set of environmental rules to achieve unified goals or indirectly influence the environment’s state. Thus, we also envision that multi-agent systems, built upon the aforementioned foundation models, will exhibit the potential to scale from individual agents to organizational entities. This talk will review the history of AI agents, briefly discuss the architectures of foundation model-based single agents in various fields, and focus on swarm intelligence for multi-agent task completion. Finally, we will explore how, as these agents are deployed, they form collective intelligence, creating a coexistence between humans and AI agents within society.