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Student Orientation Guide

Welcome to the Agentic Engineering Course. This guide tells you exactly what to do first.

Step 1: Prerequisites Checklist

Before starting, ensure you have:

[ ] Python 3.12+ installed (python --version)
[ ] Git installed (git --version)
[ ] A text editor (VS Code recommended)
[ ] At least one API key (see API Keys section below)
[ ] Terminal (PowerShell on Windows, bash on Mac/Linux)

Optional (install later when needed)

[ ] Claude Code CLI — npm install -g @anthropic/claude-code
[ ] Pi Agent CLI — npm install -g pi-coding-agent
[ ] OpenCode CLI — npm install -g @opencode/cli

Step 2: Course Structure

The course is organized into 8 modules with 13 labs:

course/
├── 00-CURRICULUM.md         ← START HERE: Full lesson plan
├── M1-FOUNDATIONS.md        ← START HERE: Module 1
├── M2-ARCHITECTURE.md
├── ... (M3 through M8)
├── labs/                    ← Hands-on exercises
│   ├── L1-first-agent/
│   ├── L2-multi-tool/
│   └── ... (13 total)
└── FIELD-MANUAL.md          ← Quick reference cheat sheet

How Each Module Works

1. Read the module file (M1-FOUNDATIONS.md)
2. Complete the lab exercise (labs/L1-first-agent/)
3. Take the quiz (ASSESSMENTS.md)
4. Move to next module

Step 3: Your First Lab

Navigate to labs/L1-first-agent/ and open starter.py:

bash

cd course/labs/L1-first-agent/
python starter.py test.txt "What is this file about?"

Don't have an API key? The mock LLM client handles this automatically. Your code runs the same way with or without a real API key.

Lab Tips

Each lab has starter.py (fill in the blanks) and solution.py (reference answer)
Try the starter first. Look at the solution only when stuck
The reasoning parameter on every tool call is not optional — it's a course requirement
Always set MAX_ITERATIONS to prevent infinite loops

Step 4: Install Skills (Optional)

After completing Module 3 (Security), install the skill kits:

bash

# Windows
powershell -File install.ps1

# Mac/Linux
bash install.sh

# Single kit
bash install.sh security

Step 5: Recommended Learning Path

Order	Module	Time	Do This
1	M1 Foundations	4-6 hrs	Read + Lab 1
2	M2 Architecture	6-8 hrs	Read + Labs 2a, 2b
3	M3 Safety	5-7 hrs	Read + Labs 3a, 3b
4	M4 Orchestration	7-9 hrs	Read + Labs 4a, 4b
5	M5 Production	5-7 hrs	Read + Labs 5a, 5b
6	M6 Economics	4-6 hrs	Read + Labs 6a, 6b
7	M7 Advanced	5-7 hrs	Read + Labs 7a, 7b
8	M8 Capstone	8-12 hrs	Build your project

Common Pitfalls

Problem	Solution
`ModuleNotFoundError: anthropic`	Run `pip install anthropic` or use mock LLM (automatic fallback)
Lab runs but produces no output	Check you called `run_agent()` at the end of the script
Tool loop never terminates	Check `MAX_ITERATIONS` is set. Default is 15.
Mock LLM returns "No input provided"	Check you're passing `messages` to `create()` not just `prompt`
YAML parse error in skills	Check indentation — YAML uses 2-space indents

Getting Help

Course content issues: Check the module file first, then the solution file
Lab errors: Run python -c "from mock_llm import MockAnthropic; print('OK')" from the labs directory
Concept questions: Module files explain concepts. FIELD-MANUAL.md is the quick reference.
Still stuck: The course README.md has a full file listing

What You'll Learn

By the end of this course, you will be able to:

Build single-tool and multi-tool agents from scratch
Implement the 6-level security ladder (L0-L5) to protect your systems
Design multi-agent systems with teams, chains, and peer-to-peer communication
Deploy agents to production with CI/CD, observability, and rollback
Optimize costs using cascade routing and evaluate performance with pass@k
Build self-improving agents that experiment and learn

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Student Orientation Guide ​

Step 1: Prerequisites Checklist ​

Optional (install later when needed) ​

Step 2: Course Structure ​

How Each Module Works ​

Step 3: Your First Lab ​

Lab Tips ​

Step 4: Install Skills (Optional) ​

Step 5: Recommended Learning Path ​

Common Pitfalls ​

Getting Help ​

What You'll Learn ​