Comparison

Approval-gated AI operations vs. read-only and autonomous AI

AI in operations splits on one question: how much may it change, and what stands between it and production? A read-only assistant answers questions. An autonomous agent acts on its own account. GAEZLA does the work of an autonomous agent, but every change waits for an operator, and the AI never holds a credential.

At a glance

Question	Read-only AI assistant	Autonomous agent with a service account	GAEZLA
Can it make the change?	Recommends. An operator makes the change by hand.	Makes the change itself.	Drafts the exact change and runs it once an operator approves.
What keys does the AI hold?	Read credentials.	A standing privileged account.	None. Credentials stay inside GAEZLA or sealed to the host that runs the job.
If it is manipulated	Gives a wrong answer.	Can act on it before anyone looks.	Can propose something wrong; cannot run it.
Best for	Teams that only want faster answers.	Narrow, well-understood, low-risk tasks.	Production estates where the AI must do real work.

A read-only assistant

It reads your telemetry and suggests what to do. It is the safest way to start, and it stops exactly where the work begins: an operator still makes every change by hand, in every console.

Good fit when the goal is faster answers, not fewer manual steps.
It still needs read credentials, and a wrong answer still costs time.
Nothing it suggests is drafted, fingerprinted or recorded as a change.

An autonomous agent with a service account

It acts on its own. That is the fastest path from alert to change, and it makes the agent one of the most privileged accounts on your network. That account takes instructions from logs, tickets and web pages.

Good fit for narrow, well-understood tasks where little can go wrong.
Its credentials are standing, and they are where the agent runs.
If it is manipulated, it can act before anyone looks.

Approval-gated AI with no credentials

GAEZLA investigates across every system you connect through each vendor's own API and drafts the change. The change waits as a proposal until an operator approves it, and it is fingerprinted at approval so it cannot be tampered with on the way. The code the AI writes has no network, and the AI never holds a credential.

An operator approves, in the console or in their MCP client, with multi-factor authentication if you require it. Approve and execute refuse machine tokens, and nothing in GAEZLA can approve a proposal.
A secret a job needs is released after approval and sealed to the one host that runs the job. The AI sees its name, never its value.
Every decision records who approved it, over which channel and what ran.

How to choose

Start from how much you can afford to go wrong and the reach the AI needs. If the AI only needs to answer, stay read-only. If a task is narrow and reversible, autonomy may be enough. If the AI must work across production systems you cannot afford to lose, put an operator and a sealed credential path between it and every change.

Ask every vendor where the AI's credentials live, and who can approve its actions.
Ask what happens when the model is given a malicious instruction inside a log line.
Ask what is recorded when a change runs, and who can see it.
Hold every vendor, including us, to the five rules of zero-trust AI operations, an open specification on GitHub.

Related operating models.

Next step

Compare it with the path you operate today.

Thirty minutes, no slide deck: bring one real incident and see what the AI reads, what it proposes, who approves it and what gets recorded. Or start a trial and run it on your own estate.

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