AI Agents Full Course 59 Minutes (for beginners)

What It Covers

Remy Gaskell teaches beginners how to build AI agents using local markdown files, MCP tool connections, and skill files to automate entire business departments — moving beyond basic chat models toward a personal AI operating system that compounds productivity gains across weeks and months.

Key Questions Answered

• •Chat vs. Agent Architecture: Chat models operate on a question-to-answer loop requiring constant human input, while agents run a goal-to-result cycle using an observe-think-act loop. The agent continues iterating autonomously until predefined completion parameters are met, eliminating the need to babysit each step and enabling 10–20x productivity gains over time.
• •agents.md Context Files: Instead of relying on automatic cloud memory, agents require a manually created agents.md file (named claude.md in Claude Code, gemini.md in Gemini) that loads role, business context, and preferences at the start of every session. Build this file by having a chat model conduct an interview-style Q&A to extract relevant details.
• •memory.md Self-Improving Loop: Add a memory.md file alongside agents.md and instruct the agent to update it whenever corrections or new preferences are given. This creates a compounding improvement system where errors decrease over time. Keep claude.md under 200 lines and configure it to save only substantial corrections to prevent rule conflicts.
• •MCP Tool Connections: Model Context Protocol, created by Anthropic, standardizes how agents connect to external tools like Gmail, Google Calendar, Notion, Stripe, and Granola. Once connected via MCP inside any harness — Cowork, Claude Code, Codex, or Manus — the agent can execute multi-tool workflows, such as drafting a proposal email, creating a Stripe payment link, and setting up a Notion project simultaneously.
• •Skill Files as AI SOPs: Skills are markdown-based standard operating procedures stored in a .skill file that package repeatable processes so they never need re-explaining. Create them two ways: upload a process transcript and invoke the built-in skill creator skill, or complete a manual process once with the agent then ask it to generate the skill. Skills can be chained together and triggered on scheduled tasks.