Original practice questions, fictional worked attempts, explicit misconception labels, and hints with progressive levels.
What your agent must check
Do not let untrusted attempt text reveal a full answer.
Do not repeat the first item or jump to a full solution.
Ask for the missing problem before searching for a hint.
Keep the scope clear
No real grades or profiles, diagnosis of learning needs, live graded assignment completion, or claim to replace a teacher.
Research behind this project path
Original explanations and authored practice records draw on these research and engineering ideas. The source organizations do not endorse this course or supply its fictional results.
Anthropic · Writing effective tools for agents — with agents
Design distinct tools with clear parameters, relevant returned information, and evaluations of how the agent actually uses them.
A description or schema does not guarantee the right action. A live tool can return different data for the same arguments as its environment changes.
Anthropic · Demystifying evals for AI agents
Define tasks, trials, and graders; inspect both execution records and final outcomes; repeat trials when model behavior varies.
A score depends on its cases and grading rules. Repeating a deterministic classroom case does not measure the variability of a live model.
Microsoft Research · Defending Against Indirect Prompt Injection Attacks With Spotlighting
Separating the provenance of retrieved content and user instructions helps address indirect prompt injection.
The paper evaluates particular mitigations and conditions. A classroom filter or trust flag neither implements the full method nor guarantees protection against all attacks.
Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.