AI Agents for Marketing: What They Do and How to Build a Custom One
By Adam Sandler, founder of The Viable Edge · Updated
An AI agent for marketing takes on the preparation in one recurring workflow, so your team spends its time on the decisions. To build a custom one, ground it in your team’s own sources, give it one job, and put people at the review points. The custom agent I build most often is a review and QA agent: it checks drafts against brand voice, claims rules and approved sources before a person sees them.
What can AI agents do for marketing?
Most marketing teams already use AI in a chat window. The work still stalls between the steps: someone pulls the numbers, someone rebuilds the context, someone rewrites a draft that missed the brief.
An agent takes on that preparation for one workflow. These are the places I have seen it start.
- Most commonReview and QA. Drafts checked against brand voice, claims rules and approved sources before a person sees them.
- From client deliveryCampaign brief preparation. Performance data and business context assembled into a brief your team reviews before anything is drafted.
- From client deliveryReporting. Recurring performance reporting pulled together from the sources you trust, with the findings left for a person to interpret.
- From client deliveryContent preparation. First drafts prepared from approved source material in your brand and voice, held for editing and approval.
- Proposed use caseRepurposing. Channel versions of approved content, prepared for review before anything is scheduled.
What an agent should not do
- Decide strategy, positioning or priorities. People decide those; the agent drafts and assembles.
- Publish, send or spend on its own. Anything like that waits for a named person on your team to approve it.
- Interpret the findings for you. A reporting agent pulls the numbers together and leaves the reading to a person.
- Run without human review and permissions. I don’t deploy autonomous agents that way.
How do you build a custom AI agent for marketing?
Each agent goes through the same framework as every engagement: Diagnose, Ground, Build, Train, Review. In practice that comes down to four moves, in this order.
- 1
Ground it in your own sources
Grounding comes first because the failure to design around is confident but ungrounded output: it sounds right but is not drawn from your team’s real sources.
The agent gets the brand and business context it needs from the documents, examples and source references you already have. A knowledge base can help, but it is not required.
- 2
Define its one job
One agent covers one agreed workflow. We diagnose that workflow first and agree what good output looks like before anything is built.
- 3
Put people at the review points
We agree the approval steps before the build starts, and anything that would publish, send or spend waits for a named person on your team to approve it.
- 4
Build it into your tools and train the owner
The agent is built around the tools your team already uses, where access is agreed, and tested on real work. Your team learns to run it and review it.
What you get
- A working agent for one agreed workflow.
- The context and sources it draws on, written down.
- The approval steps your team agreed, built in.
- Your team trained to run it and review its output.
- A review against the baseline we set at the start, with any next workflow agreed separately.
Platform agent, no-code builder or custom agent?
The three kinds differ in what the agent can see, who keeps it working, and whose approval flow it follows.
| Platform agent | No-code agent builder | Custom agent on your sources | |
|---|---|---|---|
| Setup | Turned on inside the platform that offers it | Assembled by your team in a visual builder | Built with your team around one agreed workflow and the tools you already use |
| What it knows | What that one platform holds | Whatever your team connects and writes into it | Your team’s own positioning, voice, rules and sources, written down |
| Who maintains it | The platform vendor | Whoever on your team built it | A named owner on your team, trained to run and review it |
| Where people approve | Where the platform’s built-in flow puts approval | Wherever someone adds an approval step | At the approval steps your team agreed before the build |
Which AI agent is best for marketing?
The best agent for a marketing team is the one that can see the work, draws on the team’s real sources, and fits the way the team signs off. Those criteria pick the winner, not a ranking.
A platform agent is the right call when the work happens inside that one platform, the agent needs only what that platform already holds, and its built-in approval flow matches how your team signs off.
A custom agent wins in the three opposite cases:
- The work spans tools, and no one platform sees all of it.
- The agent needs the team’s own knowledge: positioning, voice and rules.
- The team’s approval points don’t match the platform’s built-in flow.
24 of 25
Google showed an AI Overview on 24 of the 25 brand strategy, brand consistency and AI-readiness searches we track, so the answer a buyer reads first is usually written by a model.
Can AI agents be used for advertising?
Yes, for the preparation around advertising: assembling performance data and business context into a campaign brief, pulling recurring performance reporting together, and preparing channel versions of approved content for review.
Anything that would spend money or publish waits for a named person on your team to approve it.
Where to start
If one recurring workflow keeps stalling between the steps, that is the place to start. Tell me about the workflow.