Agentic AI: What it means to GrowthLoop

You hear the term everywhere you turn. Agentic AI isn't just a buzzword in marketing; it's arguably the biggest buzzword in the whole business world right now. But when a technical term gets thrown around this much, the meaning can get muddy. 

Before working with a brand that talks about agentic AI, it's worth taking a step back to find out what they actually mean when they use the term.

That's why we're devoting this article to explaining what agentic AI means to GrowthLoop. We're not just using the term to sound like we’re on the cutting edge. We have an understanding of the promises of agentic AI, as well as a philosophy around how marketers and advertisers should use it in the months and years to come.

What is agentic AI?

Agentic AI is a form of artificial intelligence that can autonomously plan, make decisions, and take action. AI agents have reasoning capabilities built in, meaning they require minimal supervision to work toward the specific goal you provide or task you assign. Notably, they can devise and execute multi-step plans supported by your data, especially if you create multiple AI agents that work together.

The promise of agentic AI for marketers

When generative AI hit the scene, the instinct many marketers had was to treat it as a creative tool. But creativity was never its strength — that's a distinctly human skill. The true value for marketers has always been in how it can automate all the grunt work and data analysis that would normally take humans days. It can produce data-driven insights in record time that humans can then use to fuel creative campaigns.

We're seeing marketers make a similar mistake with agentic AI: the most obvious, common use case isn't the best way to benefit from the technology. Having it analyze historical data and take recommended steps based only on what’s come before won’t help you compete in a market where everyone’s doing the same. To realize the technology's true potential in marketing, agentic AI must be paired with creative thinking and a spirit of experimentation.

If you want agentic AI to truly help you shine, use it to:

Break outside the status quo

Right now, many businesses use agentic AI to do the same things they already do, just faster. But AI decisioning without experimentation or exploration baked in is only based on the data you feed it. When you pair historical data with agentic AI, you get recommendations that are straightforward predictions based on past results. That's useful, but if you stop there, it binds you to the status quo.

To gain new insights, you need to introduce creativity into the loop. That requires a willingness to think outside the box and run experiments that create fresh data you can feed to the agentic AI.

Your competitors are probably all investing in agents that infuse existing functionality with more agentic capabilities. But the true value of agentic AI comes from establishing a closed-loop system that combines LLMs (large-language models), reinforcement learning, and causal decisioning — all coming together to build a smart system.

Achieve more effective personalization

One of the most exciting use cases for agentic AI is enabling personalization. An agentic system continually learns the best copy and creative to serve each customer. 

Agents combine learnings from past campaigns, results from new experiments, and real-time customer interaction data to achieve hyper-personalization. In other words, agentic AI gives us the tools to unlock the decades-old promise of one-to-one personalization.

Shift marketing focus back to the real goal

The primary goals of marketing can be boiled down to two objectives: acquiring new customers and increasing the value of existing customers. Yet for years, marketers have often faced pressure to focus on metrics that feel distant from those goals, like clicks, views, and shares. 

Agentic AI empowers marketers to think bigger. It can analyze all the smaller data points to provide high-level guidance on how to master the more important KPIs, like ROI and customer lifetime value.

What agentic AI isn't

Understanding what agentic AI can do for you means being clear-eyed about what it can't. Misconceptions about the technology abound. Here are a few things agentic AI isn't:

• A basic workflow: Some businesses build automated workflows and call them agents, to the degree that it's become something of a joke in the industry. A basic “do this, then that” workflow isn't an AI agent. An AI agent has to be capable of reasoning.

• Creative: AI agents are powerful, but they're not capable of coming up with new ideas. You can't just prompt an agent to think outside the box — it's bound by the data it can see. Humans are required to introduce creativity into the process.

• Easy to use well: A good agentic system is hard to build. Beyond inputting quality data, you have to build a lot of guardrails and context systems to get an agent to the point where it works effectively for your use case. Expect it to take time and expertise to work well.

• Capable of big results on its own: The buzz around agentic AI makes it sound like magic — like you'll immediately see measurable results as soon as you start using it. In fact, agentic AI is simply a prediction model. It's a very good prediction model, but it's still limited by the data you feed it. You need smart, creative humans in the mix introducing new ideas for your agents for them to really pay off.

• A differentiator: Everyone's using the same AI products, run on the same algorithms. These models are commoditized. They're not going to provide anything game-changing for your organization, unless you find a way to use them differently than your peers.

How to get started right with agentic AI

All the promise of agentic AI depends on using the technology effectively. For greatest success, keep a few guiding principles in mind:

1. Clarify your (realistic) goals

Agentic AI works best if you focus on building outcome-based agents. For each agent you build, establish a clear goal they should help you realize, and treat experimentation as a core part of the process to build causal data for the AI to work from. 

Be careful to make sure your goal is realistic and specific. If you tell an AI agent to reduce churn without providing additional guardrails, it may come up with the brilliantly effective idea of offering customers $1,000 off to stick around. You'd reduce churn, but at a high cost to your bottom line. Take time to run tests and experiments with each new agent, and make sure you’re introducing causal data that helps it produce fresh recommendations. 

2. Make sure you're using good data

One of the most essential lessons everyone using agentic AI should remember is: a model's outputs are only as good as the data you feed it. The data that goes in matters even more than the tuning of the model. 

Your data needs to be clean, accurate, and well-structured. But just as importantly, you want your data sets to include large-scale causality data. That means data that reflects out-of-the-box questions and scenarios, produced from large-scale experiments that include outcomes under treatment as well as outcomes under a control. 

If an employee comes to you with a novel idea that involves soliciting user-generated content on a social channel you haven’t used before, you want to be open to trying something new — even if it’s potentially risky. 

That’s the kind of thinking that creates diverse enough data sets to get increasingly effective results. To achieve that, you need a culture that encourages a mix of creative and data-driven thinking, not just one or the other. 

3. Start with narrow use cases

After hearing all the hype, you may be excited to go big when developing your first AI agent. But the technology works best when you focus on specific use cases. 

When starting out, also look for scenarios that have a high margin for error. You don't want your first agent to be tasked with processing customer credit card information or have the power to promise a huge discount you won't want to honor. 

Consider uses that are valuable but have lower stakes, like providing suggestions for ad placements or serving product recommendations in an online marketplace based on misspellings.

4. Think of it as a supplement to your humans, not a replacement

If you're looking for AI agents to fully replace humans in your organization, you may be disappointed in how much they're actually capable of on their own. But if you view them as a tool to elevate your employees — to essentially give them superpowers — you’re much more likely to be impressed with the outcome.

To use agentic AI responsibly, you need people overseeing it. They're the ones who can catch issues AI wouldn't recognize as a problem (like recognizing $1,000 discount as being bad for business). They're the ones capable of bringing fresh, creative ideas into the fold, so your agents aren't exclusively learning from old, stale data. And they're the ones with institutional knowledge that your data will never fully capture. Cut them out, and you lose access to multiple types of intelligence AI can't replicate.

5. Experiment

Don't stop trying new things. Encourage your team to think creatively and come up with experiments you can run. The more you experiment, the more data you'll gain. And the more data you have to work with, the better your agents will be at providing insights that produce results. But you have to keep introducing new ideas into the mix to avoid getting stuck in a stale loop that prevents you from venturing beyond the status quo.

6. Rethink your org structure

Figuring out how to use agentic AI well isn't just a technology or a tool problem. It's a change management problem. To truly take advantage of what the technology has to offer, you have to rethink your business processes and structure. Take a step back and ask: if you were building your organization from scratch today with agentic AI top of mind, how would the business look different?

The businesses that will perform best in the years to come are those willing to think beyond how things are done today. Consider what it would look like to create an agentic marketing organization, with human employees and agents working together at every stage of the process. That's how we believe the marketing org structure will evolve.

The future is bright

Change tends to bring discomfort, and this fast-evolving technology is causing big changes in the business world. It's easy to get distracted by the challenges, but agentic AI has the potential to bring many benefits to marketing. 

If you're overwhelmed, partner with an experienced, innovative company to learn how to introduce agentic AI strategically into your marketing mix. Working with people who have a strong vision and understand the space can help you overcome any fears and start using this technology to reach — and possibly go beyond — your goals.