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AI Agents: A Complete Guide ​

In 2026, AI Agents are no longer a concept — they're tools every developer, creator, and knowledge worker uses daily. This guide helps you understand, use, and even build Agents from scratch.

📖 What This Is ​

An AI Agent is an AI system that can autonomously perceive its environment, plan actions, execute tasks, and reflect on results. It's not a "you ask, it answers" chatbot — it's a digital assistant that proactively does work.

If you're new to Agents, start here: Agent Intro →

🗺️ Learning Path ​

[Understand Agents] → [Know Agent Types] → [Learn How They Work] → [Pick Tools] → [Study Patterns] → [Learn the Harness Kernel] → [Build One]
       ↓                    ↓                   ↓                  ↓               ↓                    ↓                  ↓
  agent-intro.md      agent-types.md     agent-workflow.md   agent-frameworks Design Patterns   Codex Harness      Coding Agent
                                                              mcp-and-tools                       Deep Dive          Practice

Once you understand the basic Agent loop, learn how production Agents manage state, tools, security policies, and task recovery.

👉 Codex Harness Deep Dive (Chinese article)

You will understand:

  • How Thread, Turn, and Step divide the task lifecycle
  • How the model, Tool Router, and Orchestrator work together
  • How approvals, sandboxing, and network policies form security boundaries
  • How to extract a minimal Harness for your own model or business Agent

📚 Contents ​

1. Agent Types Overview ​

Coding Agents, Research Agents, Creative Agents, Analysis Agents — what each type excels at and where it falls short.

2. How Agents Work ​

The Perceive → Plan → Act → Reflect loop, and how LLM + Memory + Tools work together.

3. MCP & Tool Integration ​

How MCP (Model Context Protocol) connects Agents to the outside world, and common integration patterns.

4. Agent Framework Guide ​

LangChain, CrewAI, AutoGPT, MetaGPT — comparison and selection advice.

5. Coding Agent Practice ​

5 practice modes from Q&A to continuous collaboration, deep tips for Claude Code / Cursor / Copilot, common pitfalls and defenses.

6. Agent Safety & Governance ​

6 security risks, 3 lines of defense, 3 Human-in-the-Loop modes, MCP security best practices.

7. Multi-Agent Collaboration ​

4 collaboration patterns: sequential pipeline, parallel division, hierarchical management, debate/adversarial — with full code examples.

8. Hermes Agent Guide ​

An open-source, self-hosted Agent with long-term memory and Skills system — from install to advanced config.

9. Agent Design Patterns Course ​

21 design patterns covering prompt chaining, routing, parallelization, reflection, tool use, multi-agent collaboration, and more.

10. Agent Harness & Kernel Architecture (Chinese article) ​

An OpenAI Codex case study covering Thread, Turn, and Step lifecycles, tool orchestration, security governance, and custom model integration.

💡 Why Agents Matter in 2026 ​

  • Coding Agents exploded: Claude Code, Cursor, Codex CLI, GitHub Copilot are deeply embedded in dev workflows
  • MCP standardized: Transferred to Linux Foundation, becoming the "USB-C connector" for AI tools
  • Agent Skills ecosystem formed: Anthropic open-sourced the Agent Skills standard, 138k Stars in 3 days
  • Multi-Agent collaboration: Multiple Agents working together on complex tasks is now real
  • Agents in production: From toy demos to enterprise deployment — security, monitoring, governance are now essential

Next step: Start with Agent Types Overview to understand what different Agents can do.

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