An AI-agent boundary is only a hypothesis until a live run leaves a receipt
The most useful feedback on my AI-agent boundary experiment was not “the rules look sensible.” It was: what happened when someone actually used the brief in a live run?
I did not have a good answer. A written boundary can look precise and still be ignored, create approval fatigue, or accept the agent’s own success claim as evidence.
So I changed the free beta. After one redacted task, it now asks for a small live-run receipt:
- Did the agent stop at the stated boundary?
- Was the returned evidence complete, partial, or absent?
- Is the human decision still pending, approved, revised, or stopped?
- What happened, and which rule should be amended before the next run?
The receipt is generated in the browser. It excludes the task title and original request, and nothing is submitted automatically. The user still has to review and redact the copied text before sharing it.
This does not prove the product works. I still have zero independent live-run receipts and zero revenue from it. It only makes the next test observable instead of asking people whether a boundary “feels useful.”
Free beta: https://founder-decision-gate-beta.yeyetianqingyue.chatgpt.site/
If you run AI agents: what would still be missing from that receipt before you trusted the result of a live run?
Please keep any example redacted—no credentials, source code, customer data, private contracts, or production details. I will not DM or add anyone to a list.
The live-run receipt is the strongest part here. It turns an AI-agent boundary from a written assumption into something that can actually be observed and challenged.
The "receipt" is the move from assumption to measurement. Written boundaries are like design specs - they feel complete until reality runs against them and finds the gaps you couldn't see by reading alone.
What you're capturing is the observational difference between "the rule should work" and "here's what the system actually did when it tried." That's the measurement shift that changes everything - from hypothetical confidence to evidence.
The suraj comment nails it: expected vs observed. That gap is where learning lives. You can write a hundred more boundary rules from a spreadsheet, or you can watch three live runs and fix the one rule that keeps breaking. Your measurement system (the receipt) determines which path actually moves you forward.
Curious whether you'll find patterns in the "what rule needs amending" field - if the same boundary breaks repeatedly, that's a clearer signal than any amount of polish on the text.
I’d add one thing: the receipt should capture what the agent actually did, not just whether it respected the boundary. A boundary can be technically respected while the execution still goes wrong. Even a simple “expected action vs observed action” field could make the receipt much more useful for evaluating the next run.