#Human-in-the-Loop
531 related articles
·4 min
Deep Dive into Alibaba's AgentScope 2.0: Six Core Upgrades and Agent Design Patterns Explained
Deep analysis of Alibaba's AgentScope 2.0 multi-agent framework: six core upgrades including event systems, security interception, HITL, and workspace systems, plus ReAct vs Plan-and-Execute agent design patterns.
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·3 min
6-Week AI Agent Development Learning Roadmap: A Complete Guide from Zero to Project Deployment
A systematic 6-week AI Agent development roadmap covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, and deployment for beginners to build production-ready agents.
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·3 min
Complete Guide to OpenAI Codex: From Installation & Configuration to Automation in Practice
Complete guide to OpenAI Codex covering installation, permission setup, project management, skills reuse, plugin extensions, and automation tasks to turn Codex into a true project collaborator.
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·4 min
LangGraph.js Practical Guide: A Frontend Engineer's Path to AI Agent Development
A practical LangGraph.js guide for frontend engineers covering LangGraph vs LangChain comparison, workflow vs general-purpose agent types, and layered Agent architecture design.
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Six Practical AI Automation Agent Use …
·3 min
Six Practical AI Automation Agent Use Cases: From Project Management to Sales Support
An in-depth analysis of six AI Agent automation tools covering project management, information aggregation, brand monitoring, sales support, file organization, and meeting prep for real-world workflows.
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·4 min
The Four Stages of AI Engineering Evolution: From Prompt to Loop Engineering Explained
A systematic breakdown of the four stages of AI engineering: Prompt Engineering, Context Engineering, Runtime Environment Engineering, and Loop Engineering — with core logic, bottlenecks, and real-world use cases.
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·4 min
AI Agent Learning Roadmap for Beginners: A Three-Stage Journey from Fundamentals to Hands-On Practice
A systematic AI Agent learning roadmap for beginners covering core theory, the ReAct paradigm, and multi-agent collaboration, with hands-on project suggestions.
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·4 min
AI Agent Development Learning Roadmap: A Complete Four-Stage Plan from Zero to Production
A systematic AI Agent development learning roadmap covering LLM fundamentals, ReAct paradigm, memory & tool calling, and multi-agent collaboration across four stages with project suggestions.
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·4 min
GitHub Spec-Kit in Practice: 5 Core Commands to Keep AI Coding on Track
Deep dive into GitHub's open-source Spec-Kit: 5 core commands and 2 optional checkpoints that solve AI coding drift. From setting Rules to generating code, every step makes the AI pause for your approval.
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·4 min
7-Step Framework for Deploying Commercial AI Agents: From Requirements to Production
A detailed 7-step guide to building commercial AI Agents, covering requirements, platform selection (Coze/Dify/FastGPT), prompt engineering, databases, UI, testing, and deployment.
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·3 min
Hermes Agent Practical Guide: An AI Agent That Gets Smarter the More You Use It
Deep dive into the Hermes Agent framework's core architecture, including the Skills system and steering engineering. Real-world tests show 60%+ task completion speed improvement. Complete guide covering local deployment, Feishu integration, and self-evolving learning loops.
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·3 min
LangGraph Beginner's Guide: Core Advantages, Deployment Options, and Complete Learning Path
A comprehensive guide to LangGraph's core advantages, storage mechanisms, differences from LangChain, and private deployment options for building production-ready AI agents.
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·3 min
Databricks Tech Leaders: Why the Frontier AI Ecosystem Must Be Open
Databricks co-founders Matei Zaharia and Reynold Xin discuss why the frontier AI ecosystem must be open, the Agent Cloud concept, and how open vs. closed approaches will reshape the industry.
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·3 min
Building AI Agents from Scratch: LangGraph Architecture and Practical Guide
A deep dive into AI agent principles and development practices, covering agent definitions, leading products (Deep Research, ChengPian, Manus), and the complete LangGraph + LangChain + MCP architecture.
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·3 min
AI Large Language Model Zero-to-Hero Systematic Tutorial: A Complete Guide from Basics to Practice
A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.
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·3 min
The Essential Difference Between Agent Skills and MCP: Process Layer vs. Capability Layer
Deep dive into the essential difference between Skill and MCP in AI Agent development. Skill handles the process layer for codifying workflows; MCP handles the capability layer for connecting external systems.
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·3 min
Four Core Modules of AI Agent Development: An Architecture Guide from Failure to Stable Deployment
Deep dive into four core AI Agent modules: system prompts, tool calling, RAG memory, and ReAct workflow orchestration. Solve hallucinations, loops, and build reliable agents.
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·3 min
AI Agent Learning Path from Zero: A Complete Guide from Principles to Practice
A systematic AI Agent learning path covering core principles, dev environment setup, memory management, multi-agent collaboration, and hands-on projects for beginners.
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·3 min
Loop Engineering Explained: An Engineering Approach to Making AI Coding Agents Run Autonomously in Loops
A deep dive into Loop Engineering and the Rhythmic framework: how closed-loop systems replace repetitive prompting to enable autonomous AI coding agents with state management and budget control.
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From a Single Prompt to an AI Product:…
·3 min
From a Single Prompt to an AI Product: Starting Is Easy, Shipping Is Hard
AI startups begin with a prompt, but going from idea to product means overcoming major technical, product, and business challenges. A low barrier to entry doesn't mean a low barrier to success.
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