AI

[This article is based on a talk given at Turing Community’s Large Model Tech Study Camp. Slides: Slides link, Download PDF version]

A deep dive into the design philosophy and practical strategies for AI Agents. From the dialogue pattern of chatbots to the action pattern of Agents, we systematically design and manage the information environment of Agents to build efficient and reliable AI Agent systems.

Table of Contents

1. Part 1: Paradigm Shift - From Chatbot to Agent
2. Part 2: Core Analysis of Agents
3. Part 3: Context Engineering
4. Part 4: Memory and Knowledge Systems

Part 1: Paradigm Shift - From Chatbot to Agent

From Chatbot to Agent: A Fundamental Paradigm Shift

We are undergoing a fundamental transformation in AI interaction patterns:

Chatbot Era

• 🗣️ Conversational interaction: user asks → AI answers → repeated Q&A loop
• 📚 Knowledgeable advisor: can “talk” but not “act,” passively responding to user needs
• 🛠️ Typical products: ChatGPT, Claude Chat

Agent Era

• 🎯 Autonomous action mode: user sets goal → Agent executes → autonomous planning and decision-making
• 💪 Capable assistant: can both “think” and “do,” actively discovering and solving problems
• 🚀 Typical products: Claude Code, Cursor, Manus