@seclink: The Best Paradigm for A2A: Use Model A for Planning, Model B for Execution, and Then Use Model A for Verification...
Summary
Discussing the best paradigm of A2A (Agent-to-Agent): using different large models for planning, execution, and verification respectively, and mentioning the rapid penetration of the MCP protocol in vertical industries (such as law) and the maturation of physical AI Agent toolchains.
View Cached Full Text
Cached at: 06/02/26, 07:37 PM
Best Paradigm for A2A:
Use Model A for planning, Model B for execution, and then Model A for validation…
Y11 (@seclink): 🎯 3 most notable deep information gaps
- Rapid penetration of MCP protocol in vertical industries
- Legal industry has begun adopting MCP as the AI Agent interoperability standard
- Vendors like iManage, Harvey, Legora have followed suit
- Key insight: Discussions of MCP in China are mainly focused on general scenarios, with almost zero mention of vertical industry use cases
- Maturation of physical AI Agent toolchain
- NVIDIA is bringing physical AI
Similar Articles
@dotey: Building an Agent Harness itself is no longer valuable—no matter how hard you try, you can't compete with model companies. Once the model upgrades, much of your work becomes obsolete. But building solutions on top of a mature Agent Harness has great potential. MCP only solves the connectivity problem, Skills only solves the domain knowledge problem…
The author argues that directly developing an Agent Harness is of little value because model companies will dominate, but building applications in vertical domains on top of mature frameworks still offers significant opportunities. It requires redesigning AI-native workflows, UI/UX, and data organization.
@mylifcc: This is not an ordinary large model, but a Multi-Agent Orchestration System—a small model itself that intelligently and dynamically coordinates multiple cutting-edge models such as GPT, Claude, and Gemini, autonomously assigning roles, decomposing tasks, and completing comp...
Sakana AI has released a Multi-Agent Orchestration System that uses a small model to intelligently coordinate cutting-edge large models like GPT, Claude, and Gemini to autonomously assign tasks and handle complex workloads.
@seclink: Fun fact: Currently, the specific implementation directions for multimodal large model startups typically include the following. If none of these interest you, don't follow the trend and go back to learning AI coding: 1. Game AI NPC / Agent middleware (e.g., end-cloud collaborative OmniNPC, empowering 3D character interaction and emotional storytelling...)
Summarizes several main implementation directions for current multimodal large model startups, including game AI NPC, enterprise-level multimodal Agent, content generation, embodied intelligence, and visual code assistants.
@seclink: Recently this open-source tool has been quite popular. It looks like an open-source version of DingTalk Wukong and ByteDance Aily. You can use it to implement your own agent and integrate it into the aforementioned instant messaging platforms. Some guys tweaked it and used it to demo to investors, obtaining a considerable valuation. What makes investors remember...
CowAgent is an open-source AI assistant framework based on large language models. It supports autonomous task planning, long-term memory, knowledge base, multi-model switching, and multi-channel access (WeChat, Feishu, DingTalk, etc.), enabling rapid construction and deployment of personalized AI agents.
@aiDotEngineer: The Multi-Agent Architecture That Actually Ships https://youtube.com/watch?v=ow1we5PzK-o… What does a multi-agent codin…
本文深入解析了FactoryAI的Missions多智能体架构，通过角色分工、验证合约与结构化交接机制，实现了可在生产环境中连续稳定运行数十天的自动化编码系统。该设计将软件工程瓶颈从人工执行转向人类注意力管理，为开发者提供了可落地的长期多智能体协作方案。