Student Orientation Guide

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

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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

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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

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Step 3: Your First Lab

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

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

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Step 4: Install Skills (Optional)

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

# Windows
powershell -File install.ps1

# Mac/Linux
bash install.sh

# Single kit
bash install.sh security

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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

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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

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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

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What You'll Learn

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

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