71 related articles

Learn how to build a reusable AI work team in 5 steps using Coze Expert Agents — create agents, set up projects, invite members, and assign tasks efficiently.

A systematic guide to AI Agent development from beginner to deployment, covering task planning, tool calling, memory management, learning paths, and realistic commercial monetization considerations.

How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.

Why do some still feel lost after 4 years of coding? Break down the three-stage computer learning method: build fundamentals, pick a direction, learn by doing.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

Build a RAG knowledge base from scratch using Dify's low-code platform. A hands-on Delta Force game assistant case study covering agents, knowledge base setup, and private deployment.

A complete guide to deploying Dify 1.8.0: Docker setup, environment config, five app types explained, and workflow-building tips for beginners.

A deep dive into the three core LLM job roles — Application Engineer, R&D Engineer, and Algorithm Engineer — covering academic requirements, salaries, and skill roadmaps.

A deep dive into AI agents: core concepts, how they differ from LLMs, the Agent = LLM + Workflow + Knowledge Base formula, and a comparison of Coze, Dify, LangChain, and LlamaIndex.

What is an AI agent? How does it differ from a large language model? Learn the core concepts, the Agent formula (LLM + Workflow + Knowledge Base), and how to choose between Dify, LangChain, and LlamaIndex.

A complete guide to building AI agents with DeepSeek R1: private knowledge bases using RAG, basic/advanced agent implementation, and Coze/Dify workflow tutorials.

A comprehensive guide to Coze by ByteDance: multi-agent collaboration, local tool integration, cross-platform sync, and credit system. Compare with Dify to get started fast.

A four-stage AI Agent development roadmap: from core theory and ReAct paradigm to multi-agent collaboration and production deployment. Covers DeepSeek, Coze, Dify, and more.

A clear breakdown of the four core AI Agent concepts: Function Calling, Tool, MCP, and Skill — understand the full tech stack behind intelligent agent development.

Test engineers: use the AI Skill 'Doc-based Test Case Generator' to auto-generate structured test cases from PRDs or screenshots, covering boundary values, negative scenarios, and more.

LibTV's 'Screenshot to Promo Video' Skill lets designers generate promo videos by simply uploading a mockup — no prompts, no MCP setup required.

MCP and Skills aren't alternatives — they occupy different layers of AI Agent architecture. This article breaks down Function Call, MCP, and Skills to clarify each layer's role.

A deep dive into AI-powered testing: Cursor Skills, Coze agents, and LangChain multi-agent systems for automated test case generation, BDD, and review workflows.

90% of AI beginners struggle with large language models due to misdirection, poor Prompt logic, and lack of real-world deployment skills. This guide covers the complete learning path from zero to practice.

Coze vs Dify: a deep-dive comparison covering deployment, data security, and ease of use. Find out which AI agent platform suits individual developers vs. enterprises.