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.
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)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.
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.
| 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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