This is OpenDrive Clipboard — our submission for the Google for Startups AI Agents Challenge. One sentence before anything else: Beacon records. Clipboard drafts. The licensed instructor decides.
Behind-the-wheel lessons fail in two ways. They drift into feeling like tests, or they end in a pile of corrections nobody remembers. Clipboard turns one hour of driving into a structured, evidence-informed debrief — drafted by an agent, decided by a licensed instructor.
Here is the judge workflow. Pick a synthetic one-hour drive from the curriculum. Every record on this page is sample data — no student PII, no licensing records, no DOL filings. The counter says it plainly: official records created, zero.
Run the agent and watch the tool chain: scenario intake, context retrieval, drafting, routing — a deterministic, MCP-style chain. Gemini shapes the prose. It never alters a rating, a signal value, or a score. The numbers are reproducible per scenario.
What comes back is a draft of Washington's actual drive sheet — DOL form D-T-S six-six-one, zero-four-seven — with suggested one-to-four ratings, row by row. Two rules hold the line. Rows the scenario never observed are marked NO SIGNAL — the agent does not guess. And evidence never transfers between rows: a stop-sign event cannot support a traffic-light rating.
Alongside the sheet: an eco score, a behavior snapshot, and Section three comments — each labeled synthetic, none of them a clinical assessment or an official measurement.
Then the part that matters most. Every draft dead-ends at the instructor review gate. Status: draft — instructor review required. The licensed instructor reads each row, overrides any rating, then approves, edits, rejects, or regenerates. Signatures unlock a watermarked SAMPLE packet — an audit copy and a student take-home. Not DOL official scoring. Not a legal determination. Not autonomous control.
One more thing. This is not a paper architecture. Behind the demo is a working edge stack — a Jetson reading the CAN bus, read-only, with an onboard context model at sub-millisecond inference, bench-verified across fifty miles of real driving. The public demo runs synthetic data shaped to that real output.
AI organizes the moment. The instructor leads the lesson. That boundary is the product.
OpenDrive Clipboard, from OpenDrive EDU.