In 2025, Kimi launched Agent Swarm, a system that redefines how AI can operate by creating an organizational structure that self-manages. Instead of relying on a single AI agent to handle tasks sequentially, Agent Swarm uses multiple sub-agents that work in parallel. This shifts the focus from merely scaling individual models to building a network that autonomously organizes research efforts. For instance, when tasked with investigating a topic, Agent Swarm acts like a CEO, assembling a team of researchers and analysts without needing constant oversight. The article highlights the limitations of traditional AI models, which often struggle with long-horizon tasks due to their single-agent execution model. As tasks grow in complexity, these models face constraints like context window limits and the need for history compression, leading to degraded reasoning. Agent Swarm addresses this by deploying up to 100 sub-agents simultaneously, capable of executing over 1,500 tool calls and producing results 4.5 times faster than sequential methods. This approach not only improves efficiency in tasks like broad research and document processing but also fosters productive disagreement among agents, enabling richer analyses and insights. Real-world applications of Agent Swarm demonstrate its capabilities. It can autonomously gather and categorize content, such as compiling over 200 essays into organized folders or generating comprehensive literature reviews from multiple sources. Furthermore, it can facilitate diverse perspectives on complex issues, allowing teams of experts to analyze product plans or explore various narrative styles in creative writing. This evolution in AI functionality represents a significant step towards more sophisticated, self-directed systems that can tackle demanding tasks with greater effectiveness.