Demonstrate the prototype and plan a responsible release
Make the final demo an evidence-backed story
Start with a request, retrieve a defensible shortlist, draft a proposal, inspect the total, approve the current version, and produce a simulation receipt. Then deliberately show a blocked action. A demo that includes recovery teaches more than an uninterrupted happy path.
Explain which parts are implemented: fixture retrieval, deterministic totals, state transitions, gates, and component tests. Distinguish the optional local-model extraction runner from the browser simulation. Neither is a live DoorDash checkout.
Check your understanding
Which statement accurately describes the finished course project?
Check platform access before promising integration
DoorDash’s Marketplace getting-started page says access is limited and prospective partners can apply. Its developer overview describes different services. Delivery logistics APIs are not evidence of unrestricted consumer ordering access.
For a next version, verify an authorized integration that fits the exact use case, its permitted capabilities, and operational requirements. Until then, keep a human handoff for real ordering. Do not invent an endpoint or present a simulation as a completed purchase.
Check your understanding
Match each artifact to what it demonstrates.
Finish with a go/no-go memo
Run the baseline evaluation, document what remains untested, and record your release boundary. The course prototype is ready for learning and local demonstrations; live purchasing requires more integration and operational work.
In your final reflection, explain one product tradeoff, one technical invariant, one failure you diagnosed, and one next experiment. You now have a concrete way to reason about agentic products without confusing a convincing conversation with a reliable system.
Check your understanding
State your release boundary in two sentences.
Build checkpoint
Open the Safety Lab and follow the practice above. Each lab explains its inputs, its actual logic, and its limits. Save your observations below; your check-ins and reflection remain in Product Academy.
For an AI-assisted code change, download the lab bundle from the lab page. Ask your coding assistant to explain the relevant function first, change one behavior, and add one test that could fail. Compare the result with your prediction.
Save your experiment and check-in
Record your prediction, what you tried, what happened, and the evidence for your conclusion. Name one thing you can now do independently and one uncertainty to revisit.
Key takeaways
- Demonstrate both the happy path and a protected failure.
- Make simulation and live capabilities explicit.
- Plan the next release from evidence and verified access.