What is an AI agent?
An AI agent is software that uses a language model to complete a multi-step task, such as reading data, deciding what to do next and using tools to do it. A chatbot answers a question. An agent can pull a report, notice a problem and draft a fix.
That extra ability is why approval matters. An agent that can read your ad account can, if allowed, also change it. The design question is which steps it may take alone and which need a person's yes.
Why keep a human in the loop?
People should approve actions that cost money, reach customers or are hard to reverse. Language models are capable, but they can misread context, misjudge tone or act on a wrong assumption with full confidence.
Approval also protects your accounts. Ad platforms suspend accounts for policy violations, and an automated change that slips past review can trigger one. In regulated fields such as lending, legal and finance, a person signing off on customer-facing words is often a requirement in practice.
Finally, approval builds trust. Teams adopt tools they can check. When people see the drafts, they learn where the agent is strong and where it needs guardrails.
What marketing tasks suit an AI agent?
The best tasks are repetitive, rule-based and easy to check. Good starting points include:
- Report drafts. Pull weekly numbers, write a plain-English summary and save it as a draft for review before it goes to a client or owner.
- Anomaly flags. Watch spend, conversions and cost per lead, and alert a person when something moves outside a normal range.
- Search term reviews. Suggest negative keywords from the search terms report for a person to accept or reject.
- Ad copy variations. Draft headlines and descriptions within character limits and policy rules, for human editing.
- Lead triage. Summarize new leads and suggest a priority, while sales decides who to call.
- Content briefs. Outline FAQs and page structure from real customer questions.
We have built this pattern with Google Apps Script, for example a script that drafts reports and saves them for human review instead of sending them. See our AI agents service for more.
How should approvals work?
Use a simple three-step pattern: the agent drafts, a person approves, then the system acts. Each step should be visible and recorded.
- Draft. The agent prepares the change, message or report, and explains why.
- Approve. A named person reviews it in a place they already work, such as email, Slack or a shared sheet, and approves, edits or rejects it.
- Act. Only approved items are carried out, by a step with narrow permissions.
Some low-risk actions can skip approval once they have a track record, such as tagging a lead or saving a draft. Write those exceptions down. Explore how these flows connect in our workflow explorer.
What guardrails should every agent have?
Give each agent the least access it needs, and keep a record of everything it does. These rules prevent most problems.
- Narrow permissions. Read-only access by default. Write access only to the specific tools and fields the task needs.
- Spending limits. No budget or bid changes above a set amount without approval.
- Policy checks. Run drafts against ad-platform policy rules and your brand rules before a person sees them.
- Activity logs. Record what the agent read, what it proposed, who approved it and what changed.
- Easy undo. Prefer actions that can be reversed, and keep a copy of anything before it is changed.
- Data care. Keep customer personal data out of prompts unless the task truly needs it and your privacy practices allow it.
What should stay fully human?
Keep strategy, sensitive messages and final calls on spend with people. An agent can inform those decisions, but it should not make them.
Examples include responding to an unhappy customer, approving claims in regulated ads, deciding to enter a new market and setting monthly budgets. These choices need context that rarely lives in the data alone: your relationships, your capacity and your appetite for risk.
How do you get started?
Start with one task that takes a few hours a week and has a clear right answer. Weekly reporting is a common first choice because the output is easy to check.
Run the agent alongside the manual process for a few weeks and compare results. Fix the gaps, then let the agent take over the draft while a person keeps approval. Clean data makes all of this easier, so if your numbers live in five disconnected tools, our data engineering team can join them first. For wider workflow automation, see our AI automation service.