I’m seeing more teams move from “AI can write code” to “AI can change real systems.” That second step is where most of the avoidable damage happens.
After a few messy experiments, I now use three simple deployment gates before letting an AI coding agent touch anything production-adjacent.
1) Scope gate: can the task fail small?
If the failure radius is unclear, the agent should not run with write access.
My quick check:
- Can I name the exact files, services, or environments it may touch?
- Is there a clean rollback path?
- Would a wrong action stay local, or cascade?
If I can’t answer those in two minutes, the task gets downgraded to draft-only or read-only.
2) Evidence gate: what proof must exist before merge or deploy?
A lot of “agent worked” claims are really “the tool returned success.” That’s not evidence.
For each task I now ask for concrete proof:
- test output
- diff summary
- screenshot or URL for UI changes
- exact command results
- explicit note about what was not verified
This sounds obvious, but it cuts out a huge amount of false confidence.
3) Cost gate: what is the maximum token/tool spend for this run?
Runaway loops are not just reliability bugs; they are budget bugs.
I put a ceiling on:
- total turns
- total model spend
- retries per failing step
- tool classes allowed in the session
Cheap guardrails beat heroic postmortems.
The practical rule
If a task can break prod, spend money indefinitely, or silently fake success, it should pass all three gates before autonomy expands.
I’m curious what others are using here. Do you have a preflight checklist for coding agents, or are you relying more on sandboxing plus human review?
Top comments (0)