Continuing our discussion of the Agent Economy and new methodologies for building AI agents, I spent some time this afternoon researching the design architecture of LangChain's open-source Deep Agents. Its design philosophy draws inspiration from Claude Code and is similar to @SentientAGI's recently launched ROMA (Recursive Open Source Meta-Agent) + GRID agent collaboration network architecture. Both utilize a meta-agent powered by a leading commercial LLM (Claude Sonnet4 is the preferred choice) responsible for creating, planning, and managing To-Do Lists. A routing node then distributes tasks to sub-agents for processing. Sub-agents inherit the meta-agent's features, allowing them to recursively decompose tasks and distribute them to lower-level sub-agents. Deep Agents are explicitly stateful, with a virtual file system managing data acquisition, storage, and collaboration from the MCP. This is similar to @ReiNetwork0x's agent system design, but not as complex as the latter's agent state management system. Sentient has yet to clarify whether it incorporates state management. In my personal experience, AI x Crypto agent architectures like Sentient and REI are essentially on par with state-of-the-art architectures. Unlike previous AI agent frameworks like AI16Z and Swarm, they deliver a truly usable AI agent product, not just a bunch of "shitposter" robots. AI agents are moving beyond automation (the standard paradigm: LLM + Memory + Tools) and into the initial stages of autonomy (the standard paradigm: meta-agent + state management system + integrated tools + sub-agents). AI learning will occur not only at the token level, but also at the agent level.