#Agent开发
127 related articles
Deep DivesMCP (Model Context Protocol): Solving the Three Core Pain Points of AI Tool Calling
Deep dive into how MCP (Model Context Protocol) solves three core pain points of Tool Calling: verbose descriptions, unstable invocations, and lack of unified standards.
TutorialsLangChain 1.0 Complete Guide: Architecture, Core Modules, and Learning Path
Deep dive into LangChain 1.0's three-layer architecture (LangChain, LangGraph, Deep Agents), core components like Models, Tools, and Memory, plus a complete learning path from semantic search to multi-agent collaboration.
TutorialsA Practical Methodology for Dissecting Industrial-Grade Agent Source Code with 3 IDEs Working in Tandem
Learn how to use Codex, Claude Code, and Cursor together to dissect industrial-grade Agent source code, with a reusable layer-by-layer methodology applied to OpenCloud.
TutorialsGetting Started with Agent Development: 3 Agent Types × 8 Core Mechanisms Fully Explained
A complete breakdown of AI Agent development: 3 agent types (autonomous, collaborative, orchestration-based), 8 core mechanisms, a 5-stage learning path, and framework selection guidance.
TutorialsZero2Agent: An Open-Source Job Interview Prep Tutorial for Agent Engineer Positions at Top Tech Companies
Zero2Agent is an open-source interview prep tutorial covering Agent fundamentals, LangGraph/Claude Code analysis, interview question banks, and coding practice tools for landing Agent engineer roles at top tech companies.
Tutorials2 Hours to Build, 3 Days to Earn $1000: Building AI Agents for Global Markets with Nana Banana
Bilibili creator Luffin used Nana Banana to launch 9 AI Agents in two weeks, earning $1000+. Learn the complete methodology using N8N workflows and Mirror platform for AI global monetization.
TutorialsClaude Code Source Code Analysis: A Complete Breakdown of the Startup and Bootstrap Flow
Deep dive into Claude Code's source code startup and bootstrap flow, from CLI entry's on-demand loading design and REPL startup to sentiment analysis and the Agent Loop prelude.
ResearchDeep Dive into Claude Code's Open-Source Architecture: The Design Philosophy Behind 510,000 Lines of Code
Deep analysis of Claude Code's open-source architecture: dual-loop design, 7-step tool pipeline, 4-layer token compression, memory systems, and multi-agent collaboration patterns.
Expert OpinionsThe Salary Ceiling for Agent Engineers: Two Critical Dividing Lines
Agent engineer salary gaps hinge on two dividing lines: real production deployment experience and depth of foundational theory including deep learning, fine-tuning, and reinforcement learning.
Product Reviews9 AI Search Tools Compared: Choosing the Right Search Solution for Your Agent
Hands-on comparison of 9 AI search tools including Tavily, Exa, XCrawl, and Firecrawl across search accuracy, web crawling, SERP aggregation, and special features to help developers choose the right search solution for AI Agents.
Industry InsightsDeep Dive into LangChain's Full-Lifecycle Toolchain for Agent Development
LangChain unveils a full-lifecycle toolchain for Agent development at Interrupt, covering Deep Agents 0.6, SmithDB, Context Hub, LLM Gateway, and LangSmith Engine.
TutorialsSpring AI + MCP Protocol in Practice: Tool Calling, Security Authentication, and Enterprise Deployment
Deep dive into Spring AI and MCP protocol integration, covering tool calling, OAuth security, horizontal scaling, and context optimization for enterprise AI Agent services.
TutorialsIs Context Engineering the Core of Agent Development? A Deep Dive into a High-Frequency Interview Question
Deep dive into a top LLM interview question: Is context engineering the core of Agent development? Covers five context modules, four pain points, and advanced solutions.
TutorialsClaude Code Source Code Study Guide: Efficiently Mastering Core AI Agent Development Architecture
Learn AI Agent development from Claude Code's 510K lines of source code, covering Agent Loop, context compression, multi-Agent orchestration, and two efficient study methods.
TutorialsSpring AI Agent Utils: A Java Agent Toolkit Reverse-Engineered from Claude Code's Core Features
Deep dive into Spring AI Agent Utils toolkit covering Skill modules, Ask a User Question, To Do Write, Auto Memory, and multi-Agent orchestration — empowering Java developers to build powerful AI Agents.
TutorialsHarness Engineering Deep Dive: Multi-Level Memory and Multi-Agent Collaborative Development in Practice
Deep dive into Harness Engineering: deconstructing Claude Code's multi-level memory, defense-in-depth, Hermes Agent autonomous evolution, and multi-Agent collaboration for industrial-grade AI development.
TutorialsClaude Agent SDK Python in Practice: Custom Tools, Multi-Turn Conversations & Hooks Security Control
A deep dive into Claude Agent SDK for Python: Query basics, custom Tools with MCP Server, CloudSdkClient multi-turn sessions, and Hooks security control with full code examples.
Deep DivesAI Agent Development Methodology: A Complete Guide from ReAct to Enterprise-Grade Tech Stack
A deep dive into AI Agent development methodology, from the ReAct theoretical framework to a four-layer enterprise tech stack covering model services, Agent types, LangChain, and production deployment.
TutorialsHarness Engineering: A Practical Guide to AI Development — From Prompts to Mastering AI Agents
A deep dive into Harness Engineering's three-layer architecture: Information, Constraint, and Automation layers, covering Agent failure modes, OpenAI and Anthropic best practices, and AI tool selection strategies for controlled AI development.
TutorialsFrom Programmer to Agent Developer: A Detailed Four-Stage Learning Roadmap
A systematic four-stage learning roadmap for programmers transitioning to AI Agent development, covering core theory, ReAct and classic paradigms, Prompt engineering, and hands-on projects.