Loop Engineering is a practical, hands-on course that teaches the engineering principles behind modern autonomous AI systems. Instead of stopping at prompt engineering, you'll learn the complete four-layer AI engineering stack—Prompt, Context, Harness, and Loop and understand how these layers work together to build reliable AI agents.

Throughout the course, you'll build a real AI agent using Python and Claude Code while learning the ReAct reasoning pattern, context management, verification, guardrails, stopping conditions, and essential AI safety practices required for production-ready AI systems.

This course focuses on practical engineering rather than theory. You'll watch a complete end-to-end live build, understand why autonomous agents fail, and learn how to design AI systems that can reason, act, verify, and improve without requiring constant human intervention.

What you'll learn

• Build autonomous AI agents using Claude Code

• Understand Prompt Engineering, Context Engineering, Harness Engineering, and Loop Engineering

• Learn the ReAct (Reason, Act, Observe, Repeat) agent pattern

• Build reliable AI workflows with verification and stopping conditions

• Manage context efficiently and prevent context rot

• Implement guardrails, budget controls, sandboxing, and prompt injection protection

By the end of this course, you'll understand how modern AI agents work internally and have the confidence to build your own autonomous AI workflows using industry best practices.