LLM ApplicationsAdvancedUpdated 2026-06-01🆕 Cohort 3 · just launched

AI Agent Bootcamp

24 lessons 12 hours video / illustrated text

An Agent is the key leap for LLMs from 'answering questions' to 'autonomously completing tasks.' This course covers mainstream paradigms like ReAct / LangGraph / MCP — from tool-calling protocols to multi-agent collaboration, from memory-system design to evaluation loops — building your ability to independently build production-grade Agents.

By the end of this course you will be able to:

deeply understand the four Agent paradigms and explain the design motivations and trade-offs in interviews
master engineering practice across the three frameworks LangGraph / Autogen / CrewAI
master Function Calling / MCP so you can plug in any tool quickly
understand memory-system (short-term / long-term / vector) design and selection
independently build one enterprise-grade RAG Agent project you can show in interviews
standard answers and reasoning paths for 50 high-frequency Agent interview questions

systematically organize the 4 core Agent paradigms
industrial-grade practice with LangGraph / Autogen
deep dive into Function Calling / MCP
multi-agent collaboration / memory-system architecture
50+ high-frequency Agent questions explained in depth
the course includes one end-to-end Agent project demo

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The course is organized into four modules totaling 24 lessons.

01 Agent Paradigm Foundations

6 lessons · 3 hours

What is an Agent: the leap from LLM to autonomous action28 min
The ReAct paradigm: the think–act–observe loop32 min
Plan-and-Execute and Reflection paradigms30 min
Function Calling protocol analyzed in depth34 min
Tool-use engineering: JSON Schema and error handling28 min
Paradigm-foundations quiz: 15 high-frequency interview questions30 min

02 Mainstream-Framework Practice

8 lessons · 4 hours

LangGraph: graph-structured Agent orchestration36 min
Autogen: multi-agent conversational collaboration34 min
CrewAI: role-based Agent teams28 min
The MCP protocol and the tool ecosystem32 min
LangGraph vs Autogen: the selection decision24 min
Agent debugging and observability26 min
Framework-practice quiz: 20 high-frequency interview questions32 min

03 Memory Systems & Advanced Architecture

6 lessons · 3 hours

short-term vs long-term memory design28 min
vector memory and semantic retrieval30 min
layered memory architecture: session-level / user-level / global-level32 min
multi-agent collaboration modes: debate / voting / hierarchy34 min
Agent security and privilege-escalation defense26 min
Advanced-architecture quiz: 15 high-frequency interview questions28 min

04 Evaluation Systems & Project Practice

4 lessons · 2 hours

Agent evaluation: success rate, trajectory quality, final outcome28 min
failure-mode analysis and the optimization loop26 min
end-to-end project: an enterprise-grade RAG Agent42 min
course wrap-up + mock-interview drills32 min

🎯 Who it’s for

algorithm / application engineers who want to enter the AI Agent track
job seekers currently interviewing for AI Agent roles
product / tech leads who want to evaluate Agent solutions
those who already know LLM fundamentals and want to specialize in Agents

🚀 What you’ll gain

a systematic Agent knowledge map and engineering experience
hands-on ability across the 4 mainstream frameworks
one end-to-end Agent project you can showcase
standard answers for 50+ high-frequency questions
the core edge for Agent-role interviews

📋 Prerequisites

familiar with Python / async programming
know LLM fundamentals (ideally after Full-Stack Program Module 1 or self-assessment)
ideally comfortable with basic LangChain APIs

⏰ Pace

12 hours total; recommended pace: complete in 2–3 weeks
6–8 lessons per week + project practice
Access starts immediately on enrollment; review anytime, forever

The Instructor

CAS PhD · senior algorithm engineer · sits on real hiring loops · author behind the WeChat account 「兔老板工作室」

View the full instructor profile →

It's for algorithm/application engineers entering the AI Agent track, job seekers interviewing for AI Agent roles, and product/tech leads evaluating Agent solutions. Prerequisites: familiarity with Python and async programming plus basic LLM knowledge; knowing the basic LangChain APIs is a plus.

It covers the four Agent paradigms and their design motivations, engineering practice across LangGraph/Autogen/CrewAI, the Function Calling/MCP tool protocols, short-term/long-term/vector memory-system design, plus multi-agent collaboration and evaluation loops.

You'll get a systematic Agent knowledge map and engineering experience, hands-on ability across the 4 mainstream frameworks, one showcase-ready end-to-end Agent project, and standard answers to 50+ high-frequency questions. It's 12 hours total; plan for 2–3 weeks — access opens on enrollment and is permanently viewable.

RAG is the retrieval ability that 'feeds knowledge' to a model; an Agent is 'autonomous action' — planning tasks, calling tools, remembering, and reflecting. An Agent can treat RAG as one of its retrieval tools, and the two often combine (retrieval-augmented Agents). Module 2 shows how Agents orchestrate tools like RAG, web search, and code execution.

High-frequency topics: the ReAct think–act–observe loop, Tool Calling / Function Calling internals, multi-agent collaboration, memory and long-context management, task planning and reflection, framework comparisons (LangGraph / AutoGen / Coze etc.), and Agent fault tolerance and hallucination prevention. Each module comes with a high-frequency quiz, covering principles, frameworks, and project delivery end to end.

A chatbot is usually one question, one answer, driven by prompts and context; an Agent autonomously decomposes tasks, calls external tools, executes over multiple steps, and recovers from errors. Articulating that boundary in an interview shows you understand 'autonomy.' Module 1 starts from 'the leap from LLM to autonomous action.'

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