@TheoriqAI 竞争者对比分析（Agentic AI 赛道） @TheoriqAI 聚焦构建一个由 AI 智能代理组成的链上交互系统，核心目标是打造具备推理能力、多智能体协作机制的“AI 原生操作系统层”。其最大特点是： • 多智能体系统（MAS）+ AI reasoning graph 推理引擎； • 构建链上任务分解、协调与执行机制； • 以 Rollup SDK 形式向外部提供 Agent 执行环境，具有平台属性。 @TheoriqAI 的优势： 技术深度：推理图谱系统性强，可链上执行 DAG 推理路径，支持 Agent 组合任务流。 可组合性：采用模块化 Agent 设计 + Rollup SDK，具备成为链上 Agent Layer 的潜力。 AI + Rollup 结合：利用模块化 Rollup SDK，未来可嵌入各类链或 Rollup，实现 Agent-as-a-Service。 面临挑战与对手优势 http://Fetch.ai ：项目历史时间长，数据处理基础好，在Iot方向有宪法优势 Bittensor：网络效应显著，Token激励模型成熟，已经吸引大批AI开发者。 简单总结： @TheoriqAI 更像是 Agentic AI 赛道里的“操作系统层”，它不是做单一智能体，也不是做模型训练，而是搭建一个支持多智能体、任务推理、模块组合执行的链上底座。 相比之下，Bittensor 网络大、社区强，但缺乏推理控制能力。这个赛道刚刚开始，但Theoriq 的切入点和路线图是非常有野心的。 @TheoriqAI Project Competitor Comparison Analysis (Agentic AI Track) @TheoriqAI focuses on building an on-chain interaction system composed of AI intelligent agents, with the core goal of creating an "AI-native operating system layer" equipped with reasoning capabilities and multi-agent collaboration mechanisms. Its main features include: - Multi-Agent System (MAS) + AI reasoning graph engine. - Construction of on-chain task decomposition, coordination, and execution mechanisms. - Providing an Agent execution environment to external parties in the form of a Rollup SDK, possessing platform attributes. Advantages of @TheoriqAI : Technical Depth: The reasoning graph system is highly systematic, capable of executing DAG reasoning paths on-chain, and supports agent task flow combinations. Composability: Adopts a modular agent design + Rollup SDK, with the potential to become an on-chain Agent Layer. Integration of AI + Rollup: By utilizing a modular Rollup SDK, it can be embedded into various chains or Rollups in the future, enabling Agent-as-a-Service. Challenges and Competitor Advantages: http://Fetch.ai: This project has a longer history and a solid foundation in data processing, giving it a constitutional advantage in the IoT direction. Bittensor: Exhibits significant network effects, with a mature token incentive model that has already attracted a large number of AI developers. Summary: @TheoriqAI resembles the "operating system layer" in the Agentic AI track; it does not focus on a single intelligent agent or model training but aims to build an on-chain foundation that supports multi-agent, task reasoning, and modular execution. In contrast, Bittensor has a large network and a strong community but lacks reasoning control capabilities. This track is just beginning, but @TheoriqAI entry point and roadmap are very ambitious. @KaitoAI @TheoriqAI $thq