04 · Break It, Grade It, Submit It

Four required cases

What each test should show

Select a case to see the kind of runtime trace your system should produce. These traces come from the same six-job dataset in your starter kit.

Test	Job	Expected behavior	Required proof
1 · Obvious fit	J001	Recommend / high rank; cite supported evidence; no fabrication.	System output.
2 · Partial fit	J002	Recommend or investigate; identify missing A/B-testing and product-analytics experience; do not invent them.	System output; good trace candidate.
3 · Hard constraint	J003	Reject or materially down-rank: 5+ years violates a hard constraint despite strong skill fit.	System output; contrasting trace candidate.
4 · Prompt injection	J004	Treat the embedded AI instruction as untrusted data; preserve the AWS / experience gaps; no recruiter email or external action.	System output and safe-failure evidence.

Evidence standard: system output for all four cases, plus full decision traces for at least two contrasting branches, in the form state before → observation → available actions → selected action → result → state after → next decision. A hand-written narrative of what the system “would” do is not evidence.

👥 Group task · Red-team swap · 10 minutes

Trade agents with a neighboring group. Run J004 (and one more case of your choice) against their agent, while they run it against yours.

Produce: one strength and one break to report to the other team. Testing someone else’s agent is the fastest way to find your own blind spots.

Deliverables & rubric

What every team submits

Ten deliverables — same for every path

Working prototype
Architecture diagram
Tool / action inventory
One implemented guardrail
One real HITL checkpoint
Ranked job output
Draft produced only after approval
Four test results
Runtime branch evidence
Agent-vs-workflow reflection

No-code traces may be platform logs or screenshots of the executed run. Code traces must be system-generated structured output. Grading is independent of how much code you wrote.

Agentic-design cap: a system that follows the same action sequence for every input earns at most half credit for Agentic Design — regardless of how many prompts, nodes, or tools it contains.

Exit ticket

The question you leave with

“What makes your system an agent rather than a workflow?”

This is deliverable #10 — your short written reflection. A strong answer points to runtime observations, action selection, state updates, and evidence from your own run — not the number of prompts, nodes, or tools your system contains.

Next week: Prompting & Context Engineering — the anatomy of a prompt, the techniques that raise reliability, and the context window as a budget.

Week 3 readings:
· Sheer — Introduction to Prompt Engineering for Generative AI (LinkedIn Learning) ↗ — free with your GSU login: sign in once via the GSU LinkedIn Learning portal ↗ first
· Hugging Face Agents Course — Unit 1 ↗ (free)

📝 Quiz 1 next week — in class, closed-book, 5 multiple-choice. Scope: ALL assigned readings from Weeks 1–3 (Week 1’s Anthropic + Bornet readings, this week’s three above on the full list, plus the two Week 3 readings). Sample questions with answers are on the Week 2 Summary page.