Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This article explains why Human-in-the-Loop (HITL) is essential for enterprise LLM agents, outlines common HITL patterns, and shows how LangGraph’s interrupt, resume, and checkpoint mechanisms can be used to build reliable, auditable workflows with tool‑call control and practical code examples.
This guide provides a comprehensive walkthrough of the new streamable HTTP transport mode introduced in MCP SDK v1.9.0, covering server and client configuration, core parameters, session‑id handling, sample implementations, multi‑instance deployment, and practical testing results.
This article explains why residual connections are essential in deep neural networks, describing the problems of network degradation and gradient vanishing, how shortcut paths add the input to the layer output, the requirement of matching dimensions, and the resulting stability for training large language models.
This article examines five major LLM‑driven AI agent planning and execution patterns—ReAct, Plan‑and‑Execute, Static Workflow, Static Workflow with local intelligence, and modular hierarchical planning—detailing their mechanisms, code examples, strengths, weaknesses, suitable scenarios, and optimization techniques.
The updated MCP protocol (2025‑03‑26) introduces JSON‑RPC batch processing and enhanced tool annotations, enabling AI agents to send multiple requests in a single call, improve performance, and provide richer metadata for safer, more transparent tool usage.
This article walks through the design and full implementation of an Agentic Retrieval‑Augmented Generation (RAG) system built on the MCP standard, covering the conceptual fusion of MCP and RAG, server‑side tool creation with LlamaIndex, client‑side agent construction with LangGraph, configuration files, caching strategies, code examples, and an end‑to‑end demonstration.
The MCP 2025‑03‑26 specification introduces a Streamable HTTP transport mode, an OAuth 2.1 authorization framework, JSON‑RPC batch processing, and enhanced tool annotations, detailing background, motivations, implementation changes, client‑server interaction patterns, and migration impacts for developers.
The article explains why modern AI agent systems need horizontal and vertical scaling, outlines the engineering challenges such as state consistency, scheduling, protocol design, and message efficiency, and compares three collaboration approaches—AutoGen's distributed runtime, classic RPC/MCP, and Google's A2A—while providing concrete code examples and deployment steps.
This article explains the motivation behind Google’s Agent2Agent (A2A) protocol, describes its architecture and key components, compares it with the MCP protocol, and provides a step‑by‑step demo with code showing how to build, run, and test an A2A‑enabled AI agent system.
This article surveys eight popular large‑language‑model (LLM) agent development frameworks—OpenAI Agents SDK, LangGraph, LlamaIndex, AutoGen, Pydantic AI, SmolAgents, Camel, and CrewAI—explaining each’s key features and providing concrete Python code to integrate the MCP Server for tool access.
