“I tried using AI for our marketing content, but it just doesn't sound like us.”
I hear this constantly from marketing leaders who've experimented with AI tools only to get results that feel generic or off-brand.
The problem? Most are approaching AI wrong, treating sophisticated technology like a vending machine. Insert prompt. Receive output. Hope for the best. Exceptional marketers take a different approach, viewing AI as a team member who needs proper training, clear examples, and ongoing feedback.
I‘ve seen this pattern in my work at Zappi and throughout my career in machine learning. Having worked with translation engines at HubSpot long before ChatGPT existed, I’ve developed a methodology for training AI agents that transforms them from generic content generators into true extensions of your team.
My Experience with AI Marketing Agents
For those new to this space, AI agents aren‘t just fancy chatbots. They’re specialized helpers that can work proactively on their own or as part of a team, unlike regular AI tools that just wait for instructions.
At Zappi, an AI-powered consumer insights platform, I've watched successful teams create specialized agents for specific tasks. These agents are more reliable than all-purpose AI assistants. Our customers use these specialized agents for concept development across different pieces of the product innovation process.
For example, one agent analyzes consumer feedback while another develops packaging concepts. A third focuses on in-store displays, while another handles ingredients and packaging content. Finally, a compliance agent reviews everything for policy alignment. These agents consult with each other through defined workflows, creating results dramatically better than using a single general-purpose AI.
Training allows teams to build these focused agents that can level up their workflows. Through my work at Zappi and my own experiences training AI agents for various marketing functions, I've developed a methodology that works consistently. Below, I’ll share my approach.
A Step-by-Step Guide to Training Your Marketing Agents
1. Be super clear on goals with specific examples.
The first and most crucial step is defining goals with specific context. Before I train any agent, I get painfully specific about what I want it to do. That means going beyond “help me with marketing content” and defining things like:
- What the end goal is of the piece of content I’m developing.
- What stage of the funnel I’m targeting.
- Who the reader is.
- What action I want them to take.
- What’s worked well in the past.
- What tone or format I want to use.
- What to avoid based on previous failures.
It sounds obvious, but this is where many people go wrong. If your strategy is fuzzy, your agent’s output will be too. And yes, it’s tedious. But the more clarity you feed into your training process, the better your results will be.
Time-Saving Hack: If you‘re struggling to define goals, ask a generalist AI to help develop your plan. Sometimes marketers lack full context themselves. If you don’t understand it, how will your agent? Ask those upstream questions to set your agent up for success.
2. Iterate on output and give clear feedback.
When an agent produces content that really works, I explicitly tell it, “This nails it. Use this template going forward.” I save these successful outputs as templates and training inputs for future, more specialized work.
For instance, if a LinkedIn tip sheet converts exceptionally well, you might tell your agent: “This piece of content was successful. Create a template based on what you think made it work.”
Equally important is “negative training.” When content underperforms, add examples of what to avoid. For instance, if a LinkedIn post with a specific format consistently fails to engage the audience, I show the agent an example and say, “Avoid this format. Don't do this again.” This anti-training is just as valuable as positive examples.
Over time, as you collect more examples of successes and failures, your agent starts to recognize those patterns and improve output.
3. Turn agent into agents.
Many marketers try to build one super-agent that does everything. In my experience, this approach rarely works.
Instead, I build specialist agents with clear, limited roles. It's like hiring specialists versus generalists for your team. I might have individual agents that focus solely on tasks like:
- Writing compelling hooks for LinkedIn posts.
- Recommending the best type of content asset (carousel, tip sheet, quote card, video).
- Building the actual content based on these recommendations.
- Checking everything for brand voice and compliance alignment.
You wouldn’t expect your marketer to also be your compliance specialist, right? That's how I approach agent development, too. Each agent should have its own “job description” with specialized training.
Yes, this requires more setup initially. But, it's the key to scaling without becoming the manual go-between for every task. Breaking down the workflow into specialized steps allows each agent to focus on what it does best: creating more efficient and higher-quality output.
4. Get fancy with agent-to-agent interactions.
Once those agents are up and running, connecting them is where things get interesting. This is where agent-to-agent collaboration transforms your workflow from siloed tasks to a true system.
For example, I might write a post using a template that's performing well, then hand it to my “hook agent” to create an attention-grabbing opener, and finally pass it to my “asset recommendation agent” to suggest the best supporting visual content.
You can even create a “project management agent” that oversees all these interactions, ensuring agents aren't overlapping in scope and identifying potential conflicts. Consider this as your AI team manager asking questions like: “Are there areas where we might see scope creep?” or “Could these agents be in conflict with each other?” These management agents can review your briefings to other agents and predict where overlap or confusion could happen.
Our team at Zappi has also developed a “facilitator agent,” a specialized meta-agent whose job is to oversee multi-agent interactions, keep the various agents in check, determine and understand roles, responsibilities, and implement decision trees when the input of different agents conflicts with each other.
This multi-agent approach enables hyper-personalization as you identify patterns across channels and audiences. You'll recognize that specific approaches work well on Instagram but fall flat on LinkedIn or that specific content formats resonate with one persona but not others. That’s when you can start spotting patterns, optimizing across platforms, and adapting as your audience evolves.
Retraining is Essential, Not Optional
One of the biggest myths I encounter is that AI agent training is one-and-done. In reality, it’s an ongoing process — more like onboarding and coaching than set and forget.
I retrain my agents constantly, especially with personal content projects. When something performs exceptionally well, I feed it back into the system and ask the agent to analyze what made it successful.
Sometimes, I even use AI to analyze its own best-performing outputs. That surprises people. Most assume the learning happens automatically, but it doesn’t. Just like with people, the more specific your feedback, the faster and smarter the agent becomes.
Time-Saving Hack: Most agents can absorb information well from PDFs. When you copy-paste content, you get ads, menus, and formatting that confuse the agent. Instead, print web pages to PDF — agents can better identify what‘s important. I’ve done this with LinkedIn newsletters when adding content to Claude. It's a small trick that saves significant time and creates resources you can reuse for future training.
Training Agents on Brand Voice and Tone
Here‘s a particular challenge many marketers face: How do you train an AI agent on your brand’s unique voice when most companies don't properly document that voice in the first place?
One hack I use is having an AI tool derive a brand style guide from existing content. Even before AI tools could do this for me, I manually analyzed transcripts to identify specific words and phrases unique to a company or brand.
If you don't have established content writers, try interviewing people around your company, especially founders and customer-facing employees. Those early conversations with customers often contain the DNA of your brand communication style.
Record these conversations, get a transcript, and feed that into an AI tool. Then, you've got the beginnings of brand style guidelines. When creating these guidelines, provide numerous examples showing what to do and avoid. For instance:
- Show them specific phrases: “Say this instead of that.”
- Define boundaries clearly: “Here are words we never use.”
- Provide contrasting examples: “This is well-written copy that aligns with our brand versus this poorly-written example.”
These agents operate exceptionally well with clear rules. The more specific examples and guidelines you provide, the better and faster they'll learn to recognize patterns and apply them consistently.
Your next steps depend on your situation. Large companies should refine existing documentation for AI consumption. If you have nothing documented (which is surprisingly common), create guidelines that can scale. For outdated guidelines, use this opportunity to refresh.
At Zappi, our customers upload their brand style guides and examples of approved content, often including context about their brand's values, history, and evolution. This documentation helps train AI agents to stay authentic to the brand across everything from product innovation to campaign development.
Building Compliance Into Your Agent Framework
For regulated industries, compliance isn‘t optional — it’s essential. I've found that creating dedicated compliance agents is far more effective than trying to build compliance into general marketing agents. Treat compliance as a specialized function by:
- Providing before-and-after examples of compliant content, especially with track changes and explanations.
- Documenting boilerplate language that regularly replaces non-compliant text.
- Interviewing your legal team about the most common changes they make.
Many companies we work with are in regulated spaces like alcohol and consumer packaged goods. When brands do co-marketing (like when a soft drink brand partners with an alcohol brand), they often have very different compliance guidelines. Having separate compliance agents for each brand ensures that content meets both sets of requirements.
In compliance-heavy industries, even a single hallucinated claim can carry real risk. That’s why dedicated compliance agents and human review aren’t optional.
When Humans Need to Get Involved
Despite all the capabilities AI agents offer, human involvement remains critical in three key areas.
1. Data Preparation and Hygiene
The majority of human effort goes into preparing and maintaining quality data. Your agents will only be as effective as the data they're using.
2. Process Design and Intervention Points
Humans must design how agents interact and identify necessary touchpoints. For example, when content goes off-brand after a compliance check, someone needs to make the call on priorities.
3. High-Risk and High-Visibility Content
Human review is essential for high-risk content (where errors could be costly) or high-visibility campaign assets. The level of risk and visibility determines where human touchpoints are needed.
Beyond these areas, strategy, judgment, and true creativity should remain primarily human-driven. The best approach is co-creation between humans and agents, not replacement.
Agents Don't Replace Marketers, They Scale Them
Training agents takes time. It’s iterative and sometimes tedious, but when done right, the effort is worthwhile.
- You get amplification, not replacement.
- You get speed without sacrificing strategy.
- You get scale with brand integrity intact.
And if you're a marketer with limited time and growing complexity, that’s a pretty good trade.
I’ve seen firsthand how well-trained agents can extend the reach and impact of marketers, without compromising brand or creativity.
The future of marketing isn’t a battle between humans and AI. It’s a partnership. One that expands our creative potential while freeing us to focus on what matters most. And this is just the beginning.
