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I sit down with Remy Gaskell to break down how anyone can build AI agents to run entire departments of their business. Remy walks through the core concepts: agent loops, context files, memory, MCP tool connections, and skills. We put everything together by building a fully functional executive assistant live on screen. This is a beginner-friendly crash course that covers Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw, showing that once you understand how to "drive," you can jump into any agent platform. By the end, listeners know exactly how to set up markdown-based context files, connect their everyday tools, and create reusable skills that compound over weeks and months. Key Points * Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them. * The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results. * A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time. * MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more. * Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week. * Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger. Numbered Section Summaries 1. The Agent Loop in Action Remy kicks off with a live demo, sending the same prompt — "build a minimalist portfolio site for Greg Isenberg" — to Claude Code, Codex, and Antigravity simultaneously. All three platforms run the same observe-think-act loop: research the subject, write the code, spin up a preview, and verify the result with a screenshot. The demo makes it tangible that every agent harness is just a different car with the same engine. 2. Onboarding Your Agent Like a Real Employee Remy shows that without context, an agent asked to "write me a cold email" has no idea who you are or what you sell. The fix is an agents.md (or Claude.md) file — a persistent context document loaded at the start of every session. You fill it with your role, business details, tools, and working preferences, and the result is that a two-word prompt produces a fully informed output. 3. Memory That Compounds Chat models store memory invisibly in the cloud; agents require you to build it intentionally. Remy adds a memory.md file and a simple instruction in the context file: "When I correct you or you learn something new, update memory.md." Preferences like tone, email sign-offs, and design choices persist across sessions, and errors decrease over time. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/ LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND REMY ON SOCIAL X:https://x.com/remy_gaskell Youtube: https://www.youtube.com/@aiwithremy Instagram: https://www.instagram.com/aiwithremy/

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