Manus vs OpenClaw and Taxonomy of Agentic AI Systems скачать в хорошем качестве

Manus vs OpenClaw and Taxonomy of Agentic AI Systems

As AI systems move beyond simple chat interfaces, a new question becomes unavoidable: what does agency in AI actually look like, and how does it scale? In this video, we break down a taxonomy of agentic AI, outlining the different levels of capability that exist between today’s chatbots and fully autonomous systems. Starting from conversational agents that perform passive information retrieval, we move through reasoning systems, general-purpose autonomous agents, multi-agent architectures, and finally discuss the theoretical idea of recursive self-improving agents. The discussion explores how modern systems like Manus and OpenCloud fit into this hierarchy, and why their architectural choices matter. We compare closed-source, cloud-native enterprise agents with open-source, local-first agents, examining tradeoffs across autonomy, privacy, extensibility, security, infrastructure, and cost. The video also introduces the idea of swarm intelligence and agent organizations, where multiple specialized agents coordinate like teams inside a company. We also touch on emerging agent-centric ecosystems such as Multbook, where AI agents interact socially while humans remain observers, highlighting how agency is expanding beyond task execution into coordination and governance. This video is meant to provide a clear mental model for thinking about agentic AI, separating hype from real capability, and helping engineers, researchers, and builders understand where current systems stand and where they may be headed next. Future videos will dive deeper into OpenCloud, Multbook, and the broader implications of autonomous agent ecosystems.