The most useful AI-agent boundary I’ve heard is not a risk score — it’s a protected-zone rule
I asked a solo founder in a public Indie Hackers thread how she decides when Claude must stop. Her rule was concrete: payments, the database, or a live file always come back to her first.
That is more actionable than “ask when risky” because:
- the decision owner is explicit;
- the protected surfaces can be identified before work starts;
- the evidence returned for review can be checked.
She also reviews changes against the current working file rather than an isolated snippet. That exposed a weakness in my own thinking: an execution brief should say not only when to stop, but what evidence must be returned before approval.
I’m testing a free, browser-local decision gate that generates an accountable owner, approval points, stop conditions, and a downloadable execution brief. It does not prove how an agent will behave in a real run, and it is not production authorization.
Free beta: https://founder-decision-gate-beta.yeyetianqingyue.chatgpt.site/
If you delegate coding work to an agent:
- What are your protected zones?
- What exact evidence must the agent return before approval?
- Which changes can it make without you?
Please use redacted examples only—no secrets, source code, customer data, or production access.
The protected-zone rule feels much more practical than a generic risk score. “Payments, database, live file” gives an agent a concrete boundary, while requiring specific evidence before approval makes the human review meaningful. The distinction between “when to stop” and “what to return before approval” is especially useful.