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# Free Preview: Lesson 1.1 — What Makes an Agent? ​

This is a sample lesson from Agentic Engineering the Hard Way (Module 1: Foundations). Full course includes 65 lessons, 13 labs, and 20 skill kits — all building from scratch, no black boxes.

## Lesson 1.1: What Makes an Agent? ​

Definition: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.

    Agent = LLM (reasoning engine)
          + Tools (capability surface)
          + Loop (autonomous decision cycle)

- A single LLM call with no tools = chatbot
- An LLM with tools but no loop = augmented inference
- Tools + loop + LLM = agent (it can decide what to do next)

### The Three Components ​

LLM — The reasoning engine. Given context + available tools, it decides which tool to call and with what parameters. The LLM is NOT the agent — it's the brain of the agent. Different models have different reasoning capabilities, but the core function is the same: given a situation and available actions, decide what to do.

Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases, call APIs. Each tool has a name, description, and input schema. The tool surface defines what the agent CAN do — everything outside this surface is something the agent cannot do, no matter how smart the LLM is.

Loop — The autonomous decision cycle. Think (LLM decides) → Act (tool executes) → Observe (result comes back) → Repeat. The loop is what makes it autonomous. Without a loop, you have a single decision. With a loop, you have an agent that can work toward a goal across multiple steps.

### Why This Matters ​

This definition is not academic. Every production agent failure I've seen traces back to one of these three:

Failure | Root Cause
--- | ---
Agent does something unexpected | Loop didn't terminate correctly
Agent can't do the task | Tools are insufficient for the task
Agent makes bad decisions | LLM doesn't have enough context
Agent costs too much | Loop runs too many iterations

If you understand these three components and how they interact, you can debug any agent system. If you don't, you're guessing.

## What You'll Learn in the Full Course ​

Module | Topic | Lessons | Labs
--- | --- | --- | ---
M1 | Foundations | 8 lessons | 1 lab
M2 | Agent Architecture | 7 lessons | 2 labs
M3 | Safety & Security | 7 lessons | 2 labs
M4 | Multi-Agent Orchestration | 11 lessons | 2 labs
M5 | Production Systems | 10 lessons | 2 labs
M6 | Model Economics | 7 lessons | 2 labs
M7 | Advanced Patterns | 8 lessons | 2 labs
M8 | Capstone Project | — | Build & deploy

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Note: This preview shows approximately 30% of a single lesson. Full lessons include code examples, diagrams, quiz questions, and lab exercises.