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Free Preview: Lesson 1.1 — What Makes an Agent?

This is a sample lesson from Module 1: Foundations of the FDSA Agentic Engineering Course. Full course includes 65 lessons, 13 labs, and 20 skill kits.

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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