#ReAct loop
12 related articles
·4 min
Agent Development Learning Roadmap: A Four-Stage Systematic Guide from Beginner to Practitioner
A systematic four-stage learning roadmap for AI Agent development, covering core concepts, classic paradigms like ReAct, multi-agent collaboration frameworks, and hands-on projects to master Agent development skills in 2-3 months.
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The Four Stages of AI Coding Tool Evol…
·3 min
The Four Stages of AI Coding Tool Evolution: From Code Completion to Multi-Agent Collaboration
A deep dive into the four stages of AI coding tool evolution: from code completion and chat Q&A to Agentic Coding and multi-Agent collaboration, explaining the design logic behind Claude Code, Cursor, and Codex.
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·3 min
Cursor Auto-Review Mode Explained: Smart Tiered Approval for More Efficient AI Programming
Cursor launches Auto-review mode with smart risk assessment for tiered approval. Low-risk operations auto-approve while high-risk ones need manual confirmation.
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Tutorials·2 min
Deep Dive into Claude Code's Seven-Layer Architecture: Source-Level Analysis of the ReAct Loop and Configuration-Driven Design
Deep analysis of Claude Code's seven-layer architecture, ReAct loop core principles, and configuration-driven design. Source-level deconstruction of AI Agent mechanics with best practices.
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Tutorials·3 min
Zero2Agent: 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.
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Tutorials·3 min
Hermes Agent in Practice: A Complete Breakdown from ReAct Loop to Autonomous Skill Evolution
Deep dive into Hermes Agent's four progressive cases: terminal ReAct loop, Feishu AI assistant, four-layer persistent memory, and three-stage Skill evolution with DeepSeek support.
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Tutorials
Getting Started with LangChain: Core C…
·3 min
Getting Started with LangChain: Core Concepts and Hands-On Guide for LLM and Agent Development
A systematic guide to LangChain's core features, covering LLM vs. Agent concepts, unified interface design, multi-provider support, environment setup, and hands-on code examples for AI app development.
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Tutorials·3 min
From Traditional RAG to Agentic RAG: Principles Compared and Enterprise-Grade Implementation Guide
Deep dive into traditional RAG limitations and Agentic RAG upgrades, with ChatBox source code analysis covering core tool design, intelligent decision flows, and LangGraph implementation for enterprise deployment.
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Tutorials
Enterprise AI Agent Four-Layer Archite…
·3 min
Enterprise AI Agent Four-Layer Architecture Design and PDCA Continuous Optimization Practical Guide
Deep dive into enterprise AI Agent four-layer architecture design (User, Gateway, Agent Service, Capability layers) with PDCA optimization methodology and dual manual+automated evaluation for production-grade Agent systems.
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Tutorials
Codex Getting Started Guide: Dual-Chan…
·2 min
Codex Getting Started Guide: Dual-Channel Setup with DeepSeek (China) and ChatGPT (Global)
A detailed guide to OpenAI Codex's six core capabilities with dual setup options: DeepSeek for China-based users and ChatGPT for global access.
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Deep Dives·4 min
The Complete Guide to Agentic RAG: Principles and Code Implementation
Deep dive into Agentic RAG vs traditional RAG, covering tool calling, multi-step iteration, query rewriting, with LangChain and LangGraph code examples for building intelligent retrieval systems.
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Tutorials·4 min
Agentic RAG in Practice: Architecture Deep Dive and LangChain Implementation Guide
Compare traditional RAG vs Agentic RAG architectures, explore planning, tool use, and multi-step iteration capabilities, with full LangChain/LangGraph ReAct Agent code and ChatBoss project examples.
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