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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:

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