Build AI Agents: From Zero to Production

Introducing the Series

Build AI Agents: From Zero to Production is a real-life journey that helps you build AI Agents — from the most basic concepts to complex Multi-Agent systems running in production.

Unlike the "AI & LLM" series that focuses on theory and architecture, this series is 100% hands-on — each lesson is a real project, each concept has code that can be run immediately.

🎯 Goal: Once completed, you can build and deploy a production-ready AI Agent system for any use case.

What will you learn?

Part 1: Agent Platform — Understand before you build

• Lesson 1: What is an agent? Distinguish between chatbot vs agent vs copilot
• Lesson 2: LLM APIs Masterclass: OpenAI, Claude, Gemini — master all 3
• Lesson 3: Prompt Engineering for Agents: system prompts, personas, ReAct pattern

Part 2: Function Calling & Tool Use

• Lesson 4: Function Calling — gives the agent "hands and feet" to interact with the world
• Lesson 5: Custom Tools: web search, code execution, API integration
• Lesson 6: The Agent Loop — implement the Thought-Action-Observation loop from scratch

Part 3: RAG & Memory — Give Agent memory

• Lesson 7: RAG for Agent: connect knowledge base with ChromaDB, Qdrant
• Lesson 8: Agent Memory: short-term, long-term, episodic memory architecture

Part 4: Agentic Frameworks

• Lesson 9: LangChain & LangGraph: stateful agent workflows with graph-based orchestration
• Lesson 10: CrewAI: build a "team" of AI Agents to collaborate with each other
• Lesson 11: Advanced Patterns: Planning, Reflection, Self-Correction

Part 5: MCP, A2A & Multi-Agent Systems

• Lesson 12: Model Context Protocol (MCP): universal connection standard for agents
• Lesson 13: Agent-to-Agent (A2A): protocol for agents to communicate cross-framework
• Lesson 14: Multi-Agent Orchestration: architecture & design patterns

Part 6: Production & Actual Deployment

• Lesson 15: Guardrails & Safety: protect agents from prompt injection and hallucination
• Lesson 16: Observability & Evaluation: tracing, logging, LLM-as-a-Judge
• Lesson 17: Deploy Agent to Production: FastAPI, Docker, Cloud
• Lesson 18: Capstone Project: build a complete end-to-end AI Agent Team

Input required

• Intermediate Python (async/await, decorators, classes, error handling)
• Basic understanding of LLM (know what ChatGPT/Claude API is — or complete the "AI & LLM" series)
• Computer with at least 8GB of RAM (GPU not required — most run via API)
• OpenAI/Anthropic/Google AI account (free tier is enough for most lessons)

Tools used

Python 3.11+      | Ngôn ngữ chính
OpenAI SDK         | GPT-4o, Function Calling
Anthropic SDK      | Claude, Tool Use
Google GenAI       | Gemini, Grounding
LangChain          | Chain & Agent framework
LangGraph          | Stateful graph-based workflows
CrewAI             | Multi-agent orchestration
ChromaDB / Qdrant  | Vector databases
FastAPI            | API server
Docker             | Containerization
LangSmith          | Observability & tracing

How is this series different from "AI & LLM: From Basics to Advanced"?

	AI & LLM Series	Build AI Agents
Focus	Theory + Architecture LLM	Build a practical Agent application
Object	AI Beginner	Already know LLM basics
Practice	Code illustrates concept	Actual project per lesson
Output	Understand how LLM works	Has a portfolio of Agent projects
Technology	PyTorch, Transformers	LangGraph, CrewAI, MCP, A2A