AI agents in marketing are currently being described as the next big step. After programs that write a text on request come systems that carry out several steps in a row on their own. What this is really about is an old leadership question in new clothes: what do I hand over, under which conditions, and how do I keep the overview?
That matters more than it seems. A tool that only answers stays under the control of the person asking. An agent, by contrast, acts. It searches for information, makes interim decisions and produces results that are used further. Responsibility shifts as a result, and that is exactly what most marketing departments are not yet prepared for. Their workflows, approvals and roles were designed for people who act themselves, not for systems that act on their behalf.
- AI agents carry out tasks in several steps on their own.
- Using them is a question of delegation, not just of technology.
- Good results need clear goals, clean data and defined limits.
- The brand has to be translated into rules an agent can follow.
- Human approvals remain indispensable at the decisive points.
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What AI agents in marketing really are
AI agents are software programs that act autonomously and behave in an artificially intelligent way while doing so. The range goes from simple chatbots to systems that complete complex tasks iteratively and in several steps, much like a person who breaks a task down into smaller parts.
For marketing, this second group is the interesting one. An agent could, for example, collect product information, derive texts for different channels from it, check them against a set of rules and present the result for approval. None of these single steps is new. What is new is that they are chained together without constant intervention. The human describes the goal, and the system finds its own way there, step by step and with its own interim decisions.
I find it helpful not to think of AI agents as a tool, but as very fast, very hard-working and very inexperienced colleagues. They work tirelessly, but they only know the context as well as you describe it to them. So they need what every new person on the team needs: a clear brief, good documents and someone who checks the result.
This view also changes expectations. Nobody would let a new colleague approve a campaign on their first day. You give them manageable tasks first, look closely and widen the scope as trust grows. AI agents should be treated the same way. The scope grows with proven quality, not with enthusiasm for the technology.
Where AI agents fail in practice
The first stumbling block is an unclear brief. People can fill gaps in a briefing with experience. An agent fills them with probabilities. The result often looks plausible and is still wrong, because an assumption did not hold. The more steps run one after another, the further an early mistake can travel. In the end there is a neatly worded result whose false foundation you can no longer see.
The second stumbling block is data. AI agents that access outdated or contradictory information spread these errors at great speed. Anyone who has worked with marketing automation knows the principle: automation amplifies what is there, for better or for worse.
Third, clear limits are often missing. May the agent only create drafts, or also publish? May it access customer data? May it change budgets? As long as these questions are not answered, the use is either too cautious to be useful or too generous to be safe. Both undermine the trust such a project needs.
And finally, many underestimate the effort of control. AI agents save time in execution, but they demand attention in review. Anyone who does not plan for that only moves the work to another place and wonders why the hoped for relief does not arrive.
What AI agents mean for a brand
For a brand, the central question is whether its attitude can be translated into rules. An agent cannot intuitively sense whether a sentence sounds like the brand. It needs descriptions, examples and exclusions. Work on a clearly described brand voice therefore becomes not less important, but more important.
This has a pleasant side effect. Many brands never really wrote down their rules, because experienced people had them in their heads. Using AI agents forces you to make this implicit knowledge visible. New colleagues, agencies and partners benefit from that in the end as well.
At the same time, a clean content supply chain becomes more important. When AI agents take over parts of production, it has to be clear at which point they receive material, what they do with it and where a human takes over. Without this structure you end up with a confusing mix of content whose origin nobody can trace any more.
So the brand needs both: more clarity in its rules and more discipline in its workflows.
Where I am still unsure
I am convinced that AI agents will change the work in marketing. I am unsure how quickly and to what extent. The technology is developing fast, but organisations learn more slowly. Many of the possibilities described today assume a data quality and process clarity that rarely exist in practice.
The question of responsibility is also open for me. If an agent makes a mistake that annoys a customer or has legal consequences, the responsibility lies with the company. That is clear. Less clear is how you deal with it internally, who gave the approval and how you build trust in such systems without becoming naive.
And I wonder what happens to the learning of young people on the team. Many tasks that AI agents are supposed to take over used to be the way newcomers learned their craft. If these tasks disappear, we need new ways to build experience. I do not have a finished answer to that yet. But I consider it one of the most important questions marketing leaders should be asking now.
What I take from this for myself is an attitude of careful curiosity. I want to try AI agents on real tasks and with real data, but within a frame that can absorb mistakes. I want to learn where they are reliable and where they are not. And I want to avoid letting enthusiasm for what is possible block the view of what makes sense. Delegation has always been a question of trust. That applies to machines no less than to people.