Building AI Agents that actually work (Full Course)

Understanding AI Agents vs. Traditional Chat

The fundamental shift from chat interfaces to AI agents represents a paradigm change in how businesses automate workflows. While chat models rely on stateless conversations where context must be re-entered each session, agents operate within persistent loops that observe their environment, think through problems, and take actions autonomously. This course demonstrates that all major agent platforms—Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw—run identical observe-think-act loops under the hood. The key insight is that once a builder understands the core mechanics of one agent platform, they possess the transferable knowledge to operate any other platform effectively.

The Agent Loop Mechanics

At the heart of every functional AI agent lies the agent loop, a cyclical process where the system first observes its current state, then reasons through the situation, and finally executes appropriate actions. Remy Gaskell demonstrates this concept through live demonstrations where the same prompt—"build a minimalist portfolio site"—is sent simultaneously to three different platforms. Each agent independently researches the subject, generates code, spins up a preview environment, and verifies results with screenshots. The consistency across platforms proves that the underlying architecture remains constant; the differences are largely superficial, like choosing between different cars that all have the same fundamental engine.

Context Engineering Replaces Prompt Engineering

A critical realization for agents versus chat models is the shift from prompt engineering to context engineering. Rather than crafting increasingly complex prompts to compensate for missing information, agents thrive when loaded with rich, persistent context. The practical implementation involves creating an agents.md file—a comprehensive context document that contains your role, business details, available tools, and working preferences. This file loads at the start of every agent session, ensuring that even simple two-word prompts produce fully informed, contextually appropriate outputs. When an agent receives a command like "write me a cold email," the context file prevents it from generating generic content by providing specifics about who the user is, what they sell, and their target audience.

Building Persistent Memory Systems

Unlike chat models that store memory invisibly in cloud infrastructure, agents require builders to intentionally construct memory systems. The memory.md file serves this function, capturing learned preferences, discovered workflows, and corrected decisions across multiple sessions. The implementation involves a simple instruction within the context file: whenever the agent encounters a correction or learns something new, it should update the memory.md file. Over time, this creates a self-improving loop where tone preferences, email sign-offs, design choices, and error patterns all become codified. The compound effect means that as weeks and months pass, the agent becomes increasingly accurate and aligned with the user's preferences, while manual corrections decrease substantially.

Connecting Tools Through Model Context Protocol

The Model Context Protocol, developed by Anthropic, serves as a universal translator between agents and the tools they need to accomplish work. Rather than agents being locked into proprietary tool ecosystems, MCP enables seamless integration with Gmail, Calendar, Stripe, Notion, and countless other applications. This open protocol standard means that once a tool connection is established through MCP, the agent gains the ability to read, write, and manipulate data across your entire technology stack. The result is that agents transition from being isolated language models into genuine business process automation systems capable of interacting with the actual tools your team uses daily.

Skills as Reusable Standard Operating Procedures

Skills represent the most scalable component of agent architecture, functioning as markdown-encoded standard operating procedures that can be invoked repeatedly. Rather than explaining a complex process to an agent multiple times, builders document it once as a skill file and then invoke it as needed. The course presents real-world examples demonstrating how what previously consumed four hours of manual analysis—reviewing ad performance metrics, generating insights, and compiling reports—can execute in minutes once packaged as a skill. The power multiplies as teams add three to five skills per week, creating a compounding library of automated workflows. Scheduled tasks then transform these skills into fully autonomous workflows that run on predetermined schedules without any manual trigger.

Onboarding Agents Like Team Members

The course emphasizes treating agent onboarding similarly to bringing a new human employee into an organization. Without proper context, an agent possesses no knowledge of departmental workflows, company values, or individual preferences. Proper onboarding involves structuring agents by department, creating role-specific context files, and establishing clear communication protocols. This mirrors how human teams require orientation materials, role descriptions, and access to necessary tools before they can perform effectively. The practical advantage emerges as agents equipped with department-specific context files can handle nuanced decisions that align with organizational norms and preferences without requiring constant human intervention.

From Isolated Platforms to Interoperable Ecosystems

A significant portion of the course addresses the practical reality that builders need not commit to any single agent platform exclusively. By understanding core agent mechanics, builders develop platform-agnostic skills that transfer across Claude Code, Cowork, Antigravity, and emerging platforms like OpenClaw. The course concludes by helping beginners select their first platform based on specific needs and comfort levels, while emphasizing that architectural knowledge about context engineering, memory systems, skills architecture, and MCP connections transcends any individual tool. This approach democratizes AI agent building, removing the friction of platform lock-in and enabling builders to choose tools based on present requirements rather than fearing they'll be stranded if a platform evolves or market preferences shift.