Imagine an AI agent is asked to restart a production service.
The agent calls the appropriate tool.
The command executes.
Exit code: 0.
Or perhaps the API returns:
200 OK.
The agent reports:
"𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗿𝗲𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆."
But what have we actually proven?
We know the command executed without reporting an error.
We know the API accepted and processed the request.
But do we know the service is healthy?
Do we know it reached the intended state?
Do we know its dependencies recovered?
Do we know the application it supports is functioning correctly?
And perhaps more importantly:
𝗪𝗵𝗼 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁𝗹𝘆 𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝘁𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲?
As we give AI agents more authority to perform consequential operations against enterprise systems, I think our definition of "success" may need to change.
Traditional automation often treats successful execution as the end of the workflow.
Autonomous systems may require something more:
𝗜𝗻𝘁𝗲𝗻𝘁 → 𝗘𝘅𝗲𝗰𝘂𝘁𝗲 → 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → 𝗩𝗲𝗿𝗶𝗳𝘆 → 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲
An agent shouldn't necessarily be able to declare its own work successful simply because the mechanism it invoked returned success.
There is an important difference between:
"𝗧𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆."
and
"𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗻𝗱𝗲𝗱 𝗼𝘂𝘁𝗰𝗼𝗺𝗲 𝘄𝗮𝘀 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁𝗹𝘆 𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱."
As AI agents move from recommending actions to actually changing production systems, that distinction could become increasingly important.
I'm interested in how others are approaching this.
𝗪𝗵𝗲𝗻 𝗮𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁, 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗼𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗽𝗿𝗼𝗼𝗳 𝘁𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝘂𝗰𝗰𝗲𝗲𝗱𝗲𝗱?
Is a successful tool/API response enough?
Or should independent post-execution verification become part of the control model?