Control
Control your AI.
You put an agent to work. Now control what it does, trust it when you are not looking, and know it will act the same way every time.
| Time | Agent | Action | Verdict | Latency |
|---|---|---|---|---|
| 09:41:02 | my-assistant | send_email → newsletter_draft | APPROVE | 40ms |
| 09:41:00 | my-assistant | post_to_social → launch_post | MODIFY | 31ms |
| 09:40:56 | ops-agent | order_parts → $640 order | ESCALATE | 50.5s |
| 09:40:54 | support-bot | answer_customer → refund_question | APPROVE | 36ms |
| 09:40:50 | finance-agent | move_money → $4,800 transfer | ESCALATE | 36ms |
| 09:40:48 | my-assistant | send_email → unapproved_offer | BLOCK | 35ms |
| 09:40:44 | support-bot | issue_refund → $18.00 order | APPROVE | 34ms |
| 09:40:42 | ops-agent | order_parts → wrong_part_no | BLOCK | 30ms |
Simulated feed. Actual decisions include full audit records with hash-chain verification.
You gave an AI the keys. Who is holding the wheel?
It sends the emails. It posts to your feed. It orders the parts, answers the customer, moves the money. It works while you sleep, and that is the point. But somewhere in the back of your mind sits the question you have not said out loud: what is it doing when I am not looking?
Right now, the honest answer for most people is: I hope it does the right thing. You wrote a careful instruction and you are trusting it to hold. That is not control. That is a wish.
01
Control is knowing, not hoping.
Control is simple to feel and simple to state. It is the difference between hoping your agent behaves and knowing it will. It is a line you draw once that the agent cannot cross, whether you are watching or asleep. Before it acts, something you trust checks the action against your rules and does one of four things:
- Lets it through, when the action is clearly fine.
- Fixes it and sends it on, when a small correction is all it needs.
- Holds it for you, when it wants a human to say yes first.
- Stops it cold, when it would cross a line you drew.
That is it. Control this, control that, control the one action that would have cost you a customer or a headline. Not a lecture to the AI. A hand on the wheel.
02
Trust when you are not looking.
The reason you cannot fully relax is not that your agent is bad. It is that you cannot be there for every action it takes, and you know it. Trust is what you have when you no longer have to be there.
You get that the moment there is a line the agent cannot cross without your permission. Then you can close the laptop. The overnight orders will not blow the budget, because the limit holds without you. The posts will not go out off-brand, because the check runs whether or not you are awake. You stop watching, because you no longer have to.
03
The same way, every single time.
A person has good days and tired days. Software does not. The control you set holds on the ten-thousandth action exactly as it held on the first: same rule, same check, same result. No drift, no mood, no Friday-afternoon shortcut.
That consistency is worth more than it sounds. It means you can promise a client something and know your agents will keep the promise. It means a mistake you prevented once is prevented forever, not until someone forgets. Reliability is not a feeling here; it is the same answer, every time, by design.
04
And it pays you back.
Here is the part that surprises people. Putting the rules outside the AI, instead of stuffing them into every instruction you give it, does not cost you; it gives back. The AI stops carrying the rulebook in its head on every task, so it has more room to do the actual work, and it stops wasting effort on actions that were going to be stopped anyway.
| Rules crammed into every prompt | Rules held outside, as control |
|---|---|
| The AI re-reads the rulebook on every single action | It reads the work, not the rulebook, and runs lighter |
| You pay for actions that were going to be wrong | Wrong actions stop before they cost you anything |
| About 14,700 tokens of context spent on every routine governed action | 0 tokens through a framework plugin, about 2,470 through MCP: 83–100% less |
Figures published by GaaS — see The Context Dividend.
So control is not a tax you pay for safety. It is safer and lighter at the same time: the agent does more, more accurately, and you sleep better while it does.
05
Start controlling it today.
You do not have to flip a switch and hope. Start in shadow mode: the control layer watches every action your agent takes and shows you exactly what it would have stopped, while changing nothing. You see the line working before you ever let it enforce. When you trust what you see, you turn it on. No credit card to begin.
FAQ
Every objection, answered.
What does it mean to control an AI agent?
Control is the difference between hoping your agent behaves and knowing it will. It is a line you draw once that the agent cannot cross, whether you are watching or asleep: before it acts, something you trust checks the action against your rules and lets it through, fixes it and sends it on, holds it for you, or stops it cold.
What is GaaS, in one sentence?
An external layer that checks what your AI agents are about to do and allows, fixes, holds, or blocks it against your rules, keeping a tamper-evident record of every decision.
Is it hard to set up?
No. Start in Shadow Mode with just an email; it runs the full pipeline on real actions without enforcing anything, so there is zero operational risk. A developer wires the SDK in an afternoon, and you author policies in plain language.
What does it cost?
Start free in Shadow Mode, no card. There is a free tier, then plans from $99 a month, and under a cent per governed action at scale. Nonprofits, NGOs, and veteran-owned businesses govern free for life. See pricing.
Will it slow my agents down?
Routine actions clear in well under a tenth of a second. Only high-stakes decisions take longer, and only because you asked them to.
Do I have to change my agent or my model?
No. GaaS sits outside the agent and needs no model changes and no cooperation from the agent to work.
Doesn't governance cost me tokens?
The opposite. A self-governing agent spends about 14,700 tokens of context on every routine governed action; with GaaS it spends 0 through a framework plugin (74 when an action is blocked), or about 2,470 through MCP. On a 200K-token model, self-governance fills 30% of the window after about 14 governed actions and 60% after about 28. See The Context Dividend.
I already have prompt guardrails. Why GaaS?
Prompt guardrails live inside the model, get re-read on every call, and can be argued away. GaaS is external and enforced; the agent cannot talk it out of a block.
You control the people you trust with your business.
Your AI should be no different.
Start free in shadow mode. Watch it work before you ever go live.
Prefer the article form? Read Control Your AI on the blog.