AI Agents in a Google Ads Account: Why Nothing Happens Without Approval

AI agents that can analyse an ad account and even change it are no longer a thing of the future. The interesting question is no longer whether they can. It is how you stop a misunderstood instruction from burning budget.

Since April 2026 we have been running a team of seven specialised AI agents around Google Ads for a client. How the project is built is described in the marketing AI team case study. This post is about the principles behind it — they hold even if your setup is much smaller.

Specialists instead of an all-rounder

The obvious approach is a single assistant with access to everything. That is exactly the riskiest configuration: an agent that analyses, assesses and changes at the same time is checking its own work.

So we split the work. An account analyst reads the Google Ads data, an attribution analyst the revenue data. An experiment reviewer assesses test ideas before they start. A challenger has exactly one job: finding flawed reasoning. And an opportunity scout is allowed to think more boldly — its proposals are labelled as such and challenged again when the stakes are high.

Each of these agents only gets the tools its role needs. The specialists have no write access to the ad account at all.

Separate reading from writing

The most important principle is technical, not organisational: reading and writing go through separate paths.

Read queries go through an allowlist of permitted interfaces. Only one place in the code can write — with exactly four permitted operations: create an ad group, add an ad, revise an ad, add keywords. New ad groups are always created paused, and there is no deletion path. Budgets, bids and campaign status are still changed by a human, directly in the account.

The point behind it: a boundary that only exists as a request in a prompt is not a boundary. A boundary in code holds even when the agent misunderstands the task.

Every change is a package with an expiry date

A change is prepared as a plan and validated against the Google Ads API in advance — without anything happening. A checksum binds the approval to exactly that content. The plan is valid for 30 minutes and can be executed only once.

It is only executed once a human confirms it in a dialog that defaults to "Cancel". Every attempt is recorded in a log that is only ever appended to, never overwritten.

Each of these details closes a specific gap: the checksum prevents anything in the plan from changing after approval. The expiry prevents an old plan from hitting an account that has changed in the meantime. And the dialog prevents "yes" from being the most convenient answer.

An agent whose job is to disagree

Most mistakes in campaign analysis do not come from ignorance but from shortcuts: a correlation read as a cause, a number from the wrong source, a test without criteria set in advance.

So the rule is: every recommendation needs evidence from the ad account or the database, by query, not from memory. Tests are defined before launch with success and stop criteria. And before a conclusion becomes a recommendation, the challenger tests it for flawed reasoning.

Numbers need a calculation, not an opinion

An AI agent optimises for whatever you point it at. If the target is wrong, it will reliably optimise in the wrong direction. Before you give an AI — or Smart Bidding — a target ROAS, a sober calculation pays off: from which ROAS does your account make money at all? The break-even ROAS calculator works it out from margin, order value and fixed costs.

What we measure — and what we do not

Since April, more than 500 documented work steps and over 120 specialist agent assignments have added up. Between July and September, 37 change packages went through the gate, each one confirmed individually. We have not measured hours saved, so we do not quote any. The visible effect is in the way of working: analyses come with evidence, tests with criteria, changes with a log entry.

The next step: routines that report in

Today a human usually kicks off the team's work; only a few routines run unattended so far, such as a cost reconciliation several times a day that checks itself. Next — already planned — the team works in fixed routines on its own: it analyses accounts and running tests regularly, sends proposals for approval and reports back with reports and warnings as soon as metrics leave their expected range.

The gate stays as it is. Even an automatically generated proposal is only carried out once a human confirms it.

Frequently asked questions

What technology do I need?

An agent framework with subagents — in our case Claude Code —, API access to Google Ads and a layer of your own that separates reading from writing. That layer is the most important part. And the one no vendor delivers ready-made.

Can't I just let an AI work directly in the account?

Technically yes. But then every misunderstood instruction is instantly a live change. The gate costs one click per change — and forces a deliberate look before every change.

Does this replace Smart Bidding?

No. Bidding is still handled by Google, towards targets a human sets. The agents analyse, review tests, write reports and prepare structural changes.

Is this worth it for small accounts?

The full team, not necessarily. The principles, yes: separate paths for reading and writing, approval before every change, evidence over gut feeling.

Is this compatible with Google Ads?

Access runs exclusively through the official Google Ads API — the route Google provides for tools and automation — not through the user interface. Google now even offers an official read-only access point for AI agents itself. New ads and keywords go through Google's normal review, and a human confirms every execution beforehand.

What you can do today

1. Write down which changes an AI could prepare in your account — and which never.
2. Separate access: read access for analysis, writing only through a path with approval.
3. Define success and stop criteria for every test in advance.
4. Work out your break-even ROAS in the ROAS calculator before handing a target to Smart Bidding or an agent.
5. Log every change with evidence and approval — whether a human or an AI made it.

How the team works in detail is described in the case study. If you want to use AI agents in your marketing without giving up control: how we build this is described under AI & automation — or talk it through in a first call.