Building AI Agents that actually work (Full Course)

The podcast explores how to leverage AI agents to automate business departments, moving beyond basic chat models to goal-oriented AI. Remy Gaskell explains the concept of an AI agent, contrasting it with simple question-and-answer chatbots, and details the "agent loop" of observe, think, and act. He emphasizes context engineering, using local files and structured markdown files such as agents.md and memory.md to train AI agents. Gaskell also introduces Model Context Protocol (MCP) as a translator between AI and various tools like Gmail and Notion, enabling agents to perform complex tasks. The discussion covers practical examples, including building skills for specific processes, such as ad analysis and newsletter creation, to automate repetitive tasks and improve productivity.

Outlines

Part 1: Definitions, Core Concepts

Part 2: Setup, Context, Memory

Part 3: Tools, Skills, Automation

Part 4: Implementation, Future Steps

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Greg Isenberg

Part 1: Definitions, Core Concepts

Demystifying AI Agents: From Confusing Terms to Beginner-Friendly Explanations

AI Agents Defined: Goal-Oriented Execution vs. Question-Answer Chat Models

The Four Components of an AI Agent and the Agent Harness Analogy

Part 2: Setup, Context, Memory

Demonstrating the Agent Loop in Action: Building a Website with Multiple Platforms

Onboarding AI Agents: Context Engineering and the Importance of Agents.md Files

Memory Management: Creating a Self-Improving Loop with Memory.md Files

Part 3: Tools, Skills, Automation

Connecting Tools via MCP: Enhancing Productivity and Streamlining Workflows

Skills Defined: SOPs for AI and Automating Repetitive Processes

Advanced Skill Building: Chaining Skills and Automating Entire Workflows

Part 4: Implementation, Future Steps

OpenClaw and Beyond: Choosing the Right Agent Harness and Building Powerful Agents

Building AI Agents that actually work (Full Course)

Greg Isenberg

Part 1: Definitions, Core Concepts

00:00Demystifying AI Agents: From Confusing Terms to Beginner-Friendly Explanations

Demystifying AI Agents: From Confusing Terms to Beginner-Friendly Explanations

02:07AI Agents Defined: Goal-Oriented Execution vs. Question-Answer Chat Models

AI Agents Defined: Goal-Oriented Execution vs. Question-Answer Chat Models

05:46The Four Components of an AI Agent and the Agent Harness Analogy

The Four Components of an AI Agent and the Agent Harness Analogy

Part 2: Setup, Context, Memory

10:43Demonstrating the Agent Loop in Action: Building a Website with Multiple Platforms

Demonstrating the Agent Loop in Action: Building a Website with Multiple Platforms

15:29Onboarding AI Agents: Context Engineering and the Importance of Agents.md Files

Onboarding AI Agents: Context Engineering and the Importance of Agents.md Files

23:02Memory Management: Creating a Self-Improving Loop with Memory.md Files

Memory Management: Creating a Self-Improving Loop with Memory.md Files

Part 3: Tools, Skills, Automation

31:23Connecting Tools via MCP: Enhancing Productivity and Streamlining Workflows

Connecting Tools via MCP: Enhancing Productivity and Streamlining Workflows

39:21Skills Defined: SOPs for AI and Automating Repetitive Processes

Skills Defined: SOPs for AI and Automating Repetitive Processes

47:50Advanced Skill Building: Chaining Skills and Automating Entire Workflows

Advanced Skill Building: Chaining Skills and Automating Entire Workflows

Part 4: Implementation, Future Steps

53:35OpenClaw and Beyond: Choosing the Right Agent Harness and Building Powerful Agents

OpenClaw and Beyond: Choosing the Right Agent Harness and Building Powerful Agents