Introduction to Loop Engineering (Udemy.com)

What if you could look inside an AI agent and see exactly how it thinks?

Most people use AI tools every day — but very few understand what's actually happening underneath. When an AI agent browses the web, writes and fixes code, or answers a customer support ticket end to end, it isn't doing any of that in a single shot. It's running a loop — thinking, acting, observing the result, and going again. That loop is the engine inside every AI agent you've ever heard of.

This course teaches you that engine from the ground up.

Introduction to Loop Engineering is a focused, beginner-friendly one-hour course built for people who have never studied AI formally but want to genuinely understand how modern AI agents work — not at a surface level, but at the level of the actual mechanics. By the end, you'll be able to look at any AI agent demo or job description and understand what's going on under the hood. You'll also be able to build a simple working agent loop yourself, step by step, with no prior coding experience assumed.

Why loop engineering, and why now?

The AI skill landscape has shifted fast. In 2022, the hot skill was writing better prompts. By 2024 it became feeding models better context. In 2026, the real frontier is loop engineering — designing the cycle the AI runs inside. As Boris Cherny, who leads the Claude Code team at Anthropic, put it: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." This course gets you fluent in that new language before it becomes a hiring prerequisite.

What you'll actually learn

We start with the most fundamental question: what is a loop in an AI context? You'll learn the core think-act-observe cycle that underpins every AI agent — from simple customer support bots to multi-step coding agents that write, test, and fix their own code. We use everyday analogies before we touch a single line of code, so the concept lands before the implementation does.

From there you'll explore the four loop patterns that show up in real production systems. The ReAct loop — short for Reason and Act — is the foundational pattern, backed by peer-reviewed research from Princeton and Google, and it's the architecture behind most of the AI agents you interact with today. The reflection loop lets an agent critique and improve its own output before handing it back to you. The tool-use loop is where the real-world power comes from — the agent calls external tools like web search, calculators, or databases and updates its reasoning based on what it finds. And the multi-agent loop introduces teams of specialist agents working together, with one orchestrating the others.

We then walk through three concrete business use cases — customer support agents, coding agents, and research agents — with real statistics and honest caveats about where the technology delivers and where it still struggles. You'll leave this section with a grounded, realistic view of what loops can and can't do today.

The hands-on section

At the halfway point we move into building. You'll walk through a complete beginner-level ReAct loop — every line explained in plain English, no assumptions made. Then we bring it to life in a real environment: you'll see Claude Code's /loop command running inside Cursor, the popular AI-native code editor. This is not a demo — it's a working loop you can watch execute in real time, with the agent reasoning, calling tools, and deciding when to stop. You'll see exactly what happens at each step, what the agent "sees," and how the loop terminates. No prior Cursor or Claude Code experience is needed — we walk through the setup from scratch and keep the focus on understanding the loop mechanics, not the tooling.

Loop failures and how to prevent them

This is the section most beginner courses skip — and it's where real reliability comes from. You'll learn the three failure modes that hit almost every agentic loop eventually: the infinite loop, the hallucination loop, and token blowup. Each one is explained with a real-world example and a concrete fix. The five-fix checklist you'll leave with — hard iteration caps, no-progress detection, token budgets, context summarisation, and grounded self-checks — is the difference between an agent that embarrasses you in a demo and one that works reliably in the field.

Termination, memory, and guardrails

You'll also learn how to stop a loop the right way — because stopping gracefully is just as important as starting well. The course covers all five ways a loop should be able to exit: goal achieved, max iterations reached, budget exhausted, no progress detected, and escalation to a human. You'll learn how to design memory into your loops — both the short-term scratchpad that keeps context within a task and the long-term store that persists across sessions. And you'll learn the three-layer guardrail model — input guardrails, output guardrails, and action guardrails — that turns a prototype into something you'd actually trust.

Where the field is heading

The course closes with a clear-eyed look at two trends defining the next wave: multi-agent orchestration and loop observability. You'll understand why Gartner recorded a 1,445% surge in enterprise enquiries about multi-agent systems between Q1 2024 and Q2 2025, what frameworks like LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK are actually for, and why "cost per task" is replacing "cost per token" as the metric that matters. You'll finish knowing exactly where to go next.

Who this course is for

This course is for you if you've ever used an AI tool and wondered "how does it actually do that?" It's for product managers who want to speak the language of the engineers building AI features. It's for founders evaluating whether to build or buy agent infrastructure. It's for developers who are new to AI and want a structured foundation before diving into frameworks. And it's for curious non-technical learners who want a genuinely honest, jargon-free introduction to one of the most important architectural concepts in modern AI.

What this course is not

This is not a course on prompt engineering or general ChatGPT usage. It's also not an advanced ML course — we don't cover model training, fine-tuning, or mathematics. It's a focused introduction to agentic loop design: how loops work, why they fail, and how to build and control them at a beginner level. We use Claude Code and Cursor as practical anchors for the hands-on sections, but the concepts you learn apply to any agent framework or tool.

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