“I tried to use AI for our marketing content, but it just doesn’t sound like us.”
I constantly hear this from marketing managers who have experimented with AI tools, just to achieve results that feel generic or outside the brand.
The problem? Most of them approach the AI wrong and treat demanding technology like a machine. Insert the entry. Output received. Hope for the best. Exceptional marketers follow a different approach and see AI as a team member who needs reasonable training, clear examples and ongoing feedback.
I saw this pattern in my work at Zappi and during my entire career in machine learning. After I worked with translation engines at HubSpot long before Chatgpt, I have developed a methodology for training AI agents that transformed them from generic content generators into real extensions of their team.
My experience with AI marketing agents
For those who are new in this area, AI agents are not just unusual chatbots. They are specialized helpers who can work proactively themselves or as part of a team, in contrast to regular AI tools that are only waiting for instructions.
At Zappi, a AI-driven consumer insights platform, I saw successful teams that create specialized agents for certain tasks. These agents are more reliable than all-purpose AI assistants. Our customers use these specialized agents for concept development in various parts of the product innovation process.
For example, an agent analyzes consumer feedback while another packaging concept is developing. A third focuses on IM-Store displays, while another ingredients and packaging content treats. Finally, a compliance agent checks everything for the orientation of the guidelines. These agents consult with each other via defined workflows and create the results dramatically better than a single all-purpose AI.
Through training, teams can build these focused agents that can increase their workflows. Through my work at Zappi and my own experience that AI agents trained for various marketing functions, I developed A Methodology that works consistently. In the following I will share my approach.
A step-by-step instructions for training your marketing agents
1. Be very clearly in the goals with specific examples.
The first and most important step is to define goals with a certain context. Before I train an agent, I am painfully specific about what I want to do. That means “help me help with marketing content” and to define things like:
- What the end goal of the piece of content that I develop.
- In which phase of the funnel I aim at.
- Who the reader is.
- What action do I want you to take.
- What worked well in the past.
- What sound or format I want to use.
- What should be avoided based on previous failures.
It sounds obvious, but many people go wrong here. If your strategy is out of focus, it is also the outcome of your agent. And yes, it’s boring. But the more clarity you feed into your training process, the better your results will be.
Time -saving hack: If you have difficulty defining goals, ask a generalist -KI to develop your plan. Sometimes marketers themselves lack a complete context. If you don’t understand it, how will your agent become your agent? Ask these questions up to set up your agent for success.
2. Iterize the output and give a clear feedback.
When an agent produces content that really works, I expressly tell him: “You nail. Use this template for the future.” I save these successful expenses as templates and training for future, more specialized work.
For example, if a LinkedIn -Tipp leaf converts exceptionally well, you can say to your agent: “This content was successful. Create a template based on what you believe it works.”
“Negative training” is just as important. If the content is below average, add examples of what should be avoided. For example, if a LinkedIn contribution with a certain format does not include the audience consistently, I show the agent an example and say: “Avoid this format. Do not do this again.” This anti-draining is as valuable as positive examples.
If you collect further examples of success and failures over time, your agent recognizes these patterns and improves performance.
3 .. Agent in the Agents.
Many marketers try to build a super agent that does everything. In my experience, this approach rarely works.
Instead, I build specialists with clear, limited roles. It is like an attitude specialist against generalists for your team. I could have individual agents who focus exclusively on tasks as follows:
- Writing convincing hooks for LinkedIn contributions.
- Recommend the best types of content (carousel, tip sheet, offer ticket, video).
- Create the actual content based on these recommendations.
- Check everything for branded voices and compliance orientation.
You would not expect your marketer to be your compliance specialist, right? This is how I approach agent development. Each agent should have its own “job description” with specialized training.
Yes, this requires more setup. But it is the key to scaling without becoming a manual for every task. If you disassemble the workflow into specialized steps, any agent can concentrate on what it can best do: create a more efficient and higher quality output.
V.
As soon as these agents are in operation, things become interesting. Here, cooperation with agent-zu-agent transforms your workflow of slander tasks into a real system.
For example, I could write a contribution with a template that cuts off well, and then give it to my “Hook agent” to create an attention-strong opener, and finally hand it over to my “asset recommendation agent” to suggest the best supportive visual content.
You can even create a “project management agent” who monitors all of these interactions to ensure that the agents do not overlap and identify potential conflicts. Consider this as your AI team manager, in which questions asks like: “Are there areas in which we could see how the area sneaks?” Or “could these agents contradict each other?” These management agents can check their briefings to other agents and predict where overlap or confusion can take place.
Our team at Zappi has also developed a “moderator agent”, a specialized meta-agent whose task is to monitor interactions with several agents, to keep various agents in chess, to implement roles, responsibilities, responsibilities and implement decision trees if the entry of various agents is conflict with each other.
This multi-agent approach enables hyper. You will recognize that certain approaches work well on Instagram, but fall flat on LinkedIn or that certain content formats with one person, but not to others. Then you can start recognizing patterns, optimizing platforms and adapting to your audience during the development.
Retraining is essential, not optional
One of the greatest myths I meet is that the AI agent training is and is drilled. In reality it is an ongoing process – more like onboarding and coaching than set and forgotten.
I constantly implement my agents, especially with personal content projects. If something works exceptionally well, I feed it back into the system and ask the agent to analyze what it has done successfully.
Sometimes I even use AI to analyze his own optimal outputs. That surprises people. Most assume that learning happens automatically, but it doesn’t. Just like people, the agent becomes faster and more intelligent the more specific their feedback is.
Time -saving hack: Most agents can absorb information well from PDFs. If you copy content, you will receive ads, menus and formatting that confuse the agent. Instead, websites can print on PDF – agents can better identify what is important. I did this with LinkedIn newsletter when I added Claude to content. It is a small trick that saves a lot of time and creates resources that you can reuse for future training.
Training agents for branded voice and sound
Here is a special challenge with which many marketers face: How do you train a AI agent with the unique voice of your brand if most companies do not properly document this voice?
A hack that I use is to derive a KI tool a brand style guide from existing content. Before AI tools could do this for me, I manually analyzed transcripts to identify certain words and phrases that are unique for a company or a brand.
If you have not established content authors, try to interview people in your company, especially founders and customer-oriented employees. These early discussions with customers often contain the DNA of its brand communication style.
Write down these conversations, get a transcript and feed it in a AI tool. Then you have the beginnings of the brand style guidelines. When creating these guidelines, indicate numerous examples that show what to do and avoid. For example:
- Show you specific sentences: “Say that instead of it.”
- Define limits: “Here are words that we never use.”
- Offer contrasting examples: “This is a well -written copy that matches our brand compared to this poorly written example.”
These agents work exceptionally well with clear rules. The specific examples and guidelines that you provide, the better and faster you learn to recognize patterns and to consistently use them.
Your next steps depend on your situation. Large companies should refine existing documentation for AI consumption. If you have not documented anything (which is surprisingly common), create guidelines that can scale. For outdated guidelines, take this opportunity to update yourself.
At Zappi, our customers upload their brand style leaders and examples of approved content, which often contain the context of the values, the history and development of their brand. This documentation helps the training of AI agents to remain authentic for the brand from product innovations to the development of the campaign.
Build conformity in your agent framework
Compliance is not optional for regulated industries – it is essential. I have found that the creation of dedicated compliance agents is far more effective than trying to build compliance in general marketing agents. Treat the compliance as a special function of:
- Provision of pre- and after-examples for compliant content, especially with track changes and explanations.
- Document the boiler plate language that does not regularly replace text.
- Ask your legal team about the most common changes you make.
Many companies we work with are located in regulated rooms such as alcohol and packaging goods. If brands run co-marketing (like if a brand brand works with a alcohol-free drink with an alcohol brand), they often have very different compliance guidelines. Separate compliance agents for each brand ensure that the content meets both requirements.
In compliance-strict industries, even a single hallened claim can have a real risk. Therefore, committed compliance agents and human review are not optional.
When people have to get involved
Despite all the skills that AI agents offer, the participation of humans in three key areas remains of crucial importance.
1. Data preparation and hygiene
The majority of human efforts are used in the preparation and maintenance of quality data. Your agents are only as effective as the data you use.
2. Process design and intervention points
Man must design how agents interact and identify the necessary points of contact. For example, if the content goes out of the brand after a compliance check, someone has to call priorities.
3. Content with high risk and high visibility
The human review is high visibility for high -risk content (in which errors are costly) or for campaign assets. The risk and visibility determines where human points of contact are required.
In addition to these areas, strategy, judgment and true creativity should primarily be driven by people. The best approach is the cooperation between humans and agents, not between the replacement.
Agents do not replace marketers, they scale
Training agents need time. It is iterative and sometimes boring, but if it is done correctly, the effort is worthwhile.
- You will receive a reinforcement, not the replacement.
- You get speed without affecting the strategy.
- You get a scaling with intact brand integrity.
And if you are a marketer with a limited time and growing complexity, this is pretty good trade.
I saw first -hand how well -trained agents can expand the range and effects of marketers without affecting the brand or creativity.
The future of marketing is not a struggle between people and AI. It is a partnership. One that expands our creative potential and freed us to concentrate on what is most important. And that’s just the beginning.