agentic-ai-engineering/site/labs/index.md

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Labs Overview

13 hands-on labs covering the full agentic engineering stack. Each lab has a starter.py (fill in the blanks) and solution.py (reference answer).

Lab Index

Lab Module Topic Est. Time
L1: First Agent M1 Single-tool agent from scratch 60 min
L2a: Multi-Tool Agent M2 File ops + web search 75 min
L2b: Context-Aware M2 Sliding window + summarization 60 min
L3a: Whitelist Hook M3 L4 security implementation 60 min
L3b: Verifier Agent M3 Read-only verification 75 min
L4a: Agent Chain M4 YAML pipeline 60 min
L4b: Multi-Team M4 Team config + domain locking 90 min
L5a: Observability M5 SQLite tool tracing 60 min
L5b: CI/CD M5 Golden dataset + regression gate 60 min
L6a: Eval Harness M6 pass@k evaluation 60 min
L6b: Cost Optimization M6 Cascade routing 45 min
L7a: Autoresearch M7 Self-improving experiment loop 75 min
L7b: Meta-Agent M7 Agent that builds agents 60 min

Running Labs

cd course/labs/L1-first-agent/

# Without API key (uses mock LLM automatically):
python starter.py test.txt "What is this file about?"

# With API key:
export ANTHROPIC_API_KEY="sk-ant-..."
python starter.py test.txt "What is this file about?"

# Check solution after attempting:
python solution.py test.txt "What is this file about?"

Lab Structure

Each lab has:

  • starter.py — Code skeleton with TODO markers
  • solution.py — Complete reference implementation
  • No additional files needed — all labs are self-contained

Offline Mode

All labs include automatic mock LLM fallback. No API keys required. The mock client returns realistic, deterministic responses so you can verify your code logic without paying for API calls.