You are probably the kind of reader I have in mind if: you already use ChatGPT, Claude, or another LLM; you have used GitHub or built a simple automation; AI agents and automation interest you, but governance or approval design is not your specialty; you are starting to ask, "How much of this should I let AI do automatically?"
That last question becomes important when you move from AI that suggests something to AI or automation that can actually do something next.
The Markdown is valid. Required fields are present. Automated checks return PASS.
A check can tell you that the output has the right format. It may also give you enough evidence to verify what was produced.
But that still does not answer two different questions: Has someone accepted the content? Is the system actually allowed to publish it?
In both cases, one step succeeded. But the next, higher-impact action is still not allowed.
This article is for people who are moving beyond "ask an AI, read the answer, decide manually" and starting to connect AI or automation to actions such as: changing a repository; merging code; deploying software; sending a message; publishing content; updating another system.
If you are only using ChatGPT, Claude, or another LLM as a conversational assistant and you always decide the next step yourself, the model in this article is probably more than you need.
It becomes useful when a successful check can trigger another action automatically.
This model is useful when your workflow has some combination of: multiple stages rather than one isolated check; automated checks that return a local success state such as PASS; logs, test records, screenshots, or other evidence; human or policy review; a later action with a larger consequence than the check before it, such as merging, deploying, sending, publishing, or updating another system.
If the workflow is a single, side-effect-free check with no separate approval or execution decision, this model is probably unnecessary.
The practical idea is to keep four questions separate: Result: did the thing work? Evidence: can we show that it worked? Acceptance: has the result been accepted? Production Execution: is the next production-impacting action authorized?
This is not a universal four-gate architecture. It is a thinking aid for making the meaning of PASS explicit.
Did the implementation run? Did the expected output appear? Did the test complete?
A positive result is important, but it only tells us that something happened as expected under the observed conditions.
It does not yet answer whether the result is sufficiently evidenced, formally accepted, or authorized for production. Evidence — can we show that it worked?
For example, a run may succeed, but the supporting logs, checks, screenshots, test records, or other evidence may be missing, incomplete, or not tied to the exact version that ran.
That is a stronger state than "I saw it work." Acceptance — has the result been formally accepted?