AI Agents Deep Dive
Build AI agents that think, plan, and act. Master ReAct loops, tool use, multi-agent systems, memory patterns, and production deployment with hands-on exercises.
Chatbots answer questions. Agents do work. That’s the difference — and it’s the biggest shift in AI since ChatGPT launched.
An AI agent doesn’t just generate text. It plans multi-step tasks, uses tools (browsing, coding, file management), remembers context across sessions, and decides what to do next without you holding its hand. But building one that actually works? That takes understanding the design patterns underneath.
This course takes you inside the architecture of AI agents — the loops, the memory systems, the tool interfaces, the orchestration patterns — so you can build them, evaluate them, or make smart decisions about adopting them.
You’ll master the four core components every agent needs, then dive into design patterns: ReAct loops for reasoning + acting, reflection for self-correction, and planning for complex tasks. You’ll learn how agents use tools through function calling and MCP, how multi-agent systems coordinate, and how memory patterns give agents persistence across conversations.
The final lessons cover what breaks in production — and how guardrails, evaluation suites, and observability keep agents reliable.
What You'll Learn
- Explain how AI agents differ from simple chatbots and identify the four components of an agent
- Implement ReAct, Reflection, and Planning design patterns for different agent tasks
- Design tool-use interfaces using function calling, MCP, and structured outputs
- Build multi-agent systems with supervisor, pipeline, and peer-to-peer orchestration
- Apply memory patterns (buffer, summary, vector store) to give agents persistent context
- Evaluate agent reliability using test suites, guardrails, and observability traces
After This Course, You Can
What You'll Build
Course Syllabus
Prerequisites
- Basic understanding of AI prompting (our Prompt Engineering course recommended)
- Familiarity with APIs and JSON
- No coding required for concepts — optional coding exercises use Python
Who Is This For?
- Developers who want to build AI agents and need to understand the architecture before picking a framework
- Product managers evaluating agent capabilities for their roadmap
- AI enthusiasts who use agents daily and want to understand what's happening under the hood
- Technical leaders deciding between LangGraph, CrewAI, Claude Agent SDK, or building custom — this course is framework-agnostic
Frequently Asked Questions
Do I need to know how to code to take this course?
No. The core concepts (design patterns, architecture, tool use) are explained without code. Optional exercises use Python, but you can skip them and still learn the material.
Which AI agent framework does this course teach?
This course is framework-agnostic. You'll learn patterns that apply to LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, and any future framework. We compare frameworks so you can choose the right one.
Is this course about building chatbots?
No — it goes far beyond chatbots. AI agents can use tools, browse the web, write code, manage files, and coordinate with other agents. This course covers the architecture and patterns behind those capabilities.
What's the difference between this and the Prompt Engineering course?
Prompt Engineering teaches you to write better prompts. This course teaches you to build systems where AI acts autonomously — using tools, planning multi-step tasks, and managing its own memory. Think of it as the next level.
