A free, visual, build-it-to-learn-it site for Anthropic's CCA-F exam.
Explainers you drive, quizzes that talk back, code you run (Colab or local),
and projects you can put on a résumé.
← a real agent loop, running. You'll build one of these.
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interactive explainers
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quiz questions
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runnable exercises
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portfolio builds
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exam traps decoded
🚀 New here? Play before you read.
In The Agent Hangar, an AI agent is a
spaceship: master each part in a ~1-minute mini-game (the model = reasoning core, tools = the robotic
arm, context = cargo hold, budget = fuel, guardrails = shields), assemble the ship, then fly
missions where every decision point is a real exam judgment call — wrong picks cost hull and teach the
trap. Then the explainers, quizzes, and code make it stick.
Each step is learn (drive the explainers) → build (run/ship something) → prove (drills). The
sequence is deliberate: you need prompts and tools before agents make sense. Full pacing in the
6-week study plan.
Interactive scenario MCQs with instant feedback, "why the distractor is wrong" explanations, and best-score tracking. (Prefer reading? The printable drills have the same questions.)
Run the first cell — it installs the SDK and asks for your API key with a hidden prompt
(get a key).
Run the 10 exercises top to bottom. Total cost: a few cents on Haiku.
🖥️ VS Code — Copilot Chat or Claude Code
Learn inside your editor
Create a folder, `pip install anthropic`, export your key, and work through
the exercises as .py files.
Stuck? Ask Copilot Chat (pick a Claude model) or the Claude Code extension to explain
the error — you're literally using the Domain 3 material while studying it.
This explainer maps every
editor feature to the API concept underneath.
⌨️ Terminal — Claude Code CLI / claude.ai
Interactive & agentic
Domain 3 is hands-on by nature: install the Claude Code CLI and do the CLAUDE.md / hooks /
headless exercises in a real repo.
Use claude.ai to test prompt-engineering patterns interactively before coding them.
Projects 1–6 run from any of the three lanes — each brief says what it needs.
Which model, when — with real numbers
"Use the cheapest capable model" only sticks when you've seen the actual math. The
model-selection explainer now has a
cost calculator: dial in your volume and token sizes and watch the monthly bill for Haiku vs Sonnet vs
Opus — e.g. classifying 1M support tickets is ≈ $650 on Haiku vs ≈ $9,750 on Opus for
near-identical accuracy on that task. Three worked case studies show how an architect actually decides.