Agentic AIs—What It Is and Why It’s the Next Big Thing for Marketers

Over the past decade, artificial intelligence has fundamentally changed the way we approach marketing. This may sound like a tired cliché, but it’s hard to argue against.

What began as niche and limited solutions used by tech-savvy pros has evolved into complex and ubiquitous tools accessible just about anyone. AI is everywhere, and even people who struggle with computer literacy can use chatbots.

The scope of tasks has grown too. First, we had simple automation tools handling repetitive tasks. Now, these systems analyze customer data, generate creative content, optimize campaign performance, and perform dozens of other tasks. 

Today, 56% of marketers actively use AI in their processes. This number will undoubtedly grow as more advanced solutions become available.

None of this happened overnight. We've seen a steady progression from rule-based marketing automation to predictive analytics to generative AI systems.

And now, we’re witnessing another major shift: agentic AI—artificial intelligence that goes beyond low-level tasks. It doesn't just create content—it can make strategic decisions about your campaigns. 

Things like determining the optimal channel mix, adjusting budgets in real time, and personalizing customer journeys. All without constant human oversight.

What is Agentic AI

This technology represents a significant leap forward from the AI systems most marketers use today.

Unlike generative AI, which creates content when requested but relies entirely on human guidance, an agentic AI can independently evaluate situations, formulate strategies, and execute them without constant supervision. 

With generative AI, you need to have a clear goal and then provide it with instructions on every step towards it. Agentic AI, on the other hand, can take these steps independently once it’s provided with the endpoint objective.

Three key characteristics define these systems:

1. Autonomy: Agentic AI can operate independently, making decisions based on available data rather than requiring step-by-step human guidance.
2. Goal orientation: Agentic AI works toward specific objectives, prioritizing actions that contribute to desired outcomes.
3. Learning capability: Agentic AI continuously improves by analyzing the results of their actions and adjusting strategies accordingly.

Let’s say you want to run an email campaign. If you use current AI tools, you will have to prompt them separately to write each email in the sequence, come up with segments, optimize send times, and so on. 

Meanwhile, agentic AI will take the initial input from you and then proceed to perform all tasks independently and with minimal supervision. This transforms AI from an eager but clueless marketing trainee—who needs guidance at every step—into a capable and self-sufficient specialist. 

The Five Stages of Evolution Towards AGI—And Where the Agentic AI Fits In

Every advancement in the world of AI has one simple goal—to create an artificial general intelligence (AGI). This is AI capable of anything that humans do—and more.

OpenAI’s Sam Altman outlined a five-stage roadmap that explains how we can reach true AI. 

And while the road to AGI isn’t a short or easy one, Altman’s framework shows us why agentic AI is so important. It consists of five stages.

Stage 1: Chatbots (Conversational AI)

The first stage is the most familiar to us. It involves AI systems capable of conversations with humans, such as customer service bots and language models like ChatGPT. These systems excel at answering questions, following instructions, and generating content based on specific prompts.

However, they are not exactly intelligent—conversational AIs are prone to errors and can produce gibberish. That’s why these systems require close observation and constant proofing.

This approach started to gain traction at the end of 2022, and is widely used by a large portion of businesses.

Stage 2: Reasoning AI

These systems solve complex problems by analyzing data and applying logic. They demonstrate human-like reasoning and can work through problems step-by-step, producing smarter replies that are beyond the scope of regular chatbots.

Reasoning systems still require human direction to act on their insights. 

Examples of reasoning AI include OpenAI’s o1 and o3 models, Antropic’s Claude 3.7 Sonnet, and DeepSeek R1. These models began appearing in late 2024 and are quickly gaining popularity.

Stage 3: Agentic AI

Here comes our hero. Agentic AI can not only reason but also take autonomous actions to achieve predefined goals. These systems can make decisions independently, execute them, and learn from the outcomes to improve future performance.

Unlike the previous two stages, agentic AIs don’t require constant human intervention.

The transition from reasoners to agents may happen relatively quickly. And this can become a major shift in how we interact with AI. 

Stage 4: Innovation AI

Innovators are AI systems that will actively assist with invention and creativity, generating novel solutions to problems and collaborating with humans on tasks requiring imagination and creativity.

This stage could revolutionize industries by speeding up research and innovation processes.

Stage 5: Organizational AI

The final stage represents AI systems capable of performing the work of an entire organization, managing and executing a wide range of tasks without a need for oversight

At this stage, we’ll achieve AGI, where AI surpasses human performance in most tasks.

We’re currently embracing Stage 2 and slowly moving towards Stage 3, as reasoning AI systems begin to incorporate autonomous action capabilities. This will have a major impact on marketing automation and beyond.

How Does Agentic AI Work?

Agentic AI is the latest advancement in a very intricate and complex field. But you don’t need to be a data scientist to understand the basic workflow.

These systems operate based on four interconnected steps.

The first is perception and data gathering. At this step, the agentic AI continuously collects and processes information from various sources. These can include (but not limited to) customer data, campaign performance metrics, and market trends.

Next, we have the reasoning and decision-making engine. Here, the system evaluates incoming data against your goals. Using sophisticated reasoning capabilities, it identifies patterns and opportunities, weighs potential actions, and determines the optimal next steps based on predicted outcomes.

The action component implements these decisions autonomously. For example, it can proceed to adjusting ad spend, personalizing content, or modifying campaign parameters.

And lastly, we have the learning and adaptation system. AI learns from outcomes, adapting strategies in real time to improve performance and achieve goals.

This continuous learning loop is the main strength of agentic AIs. After each action, the system monitors results and incorporates this feedback to improve future decisions. 

For example, when optimizing an email campaign, it might notice that changing send times for a specific customer segment increases open rates. It will then apply this learning to campaigns, steadily improving performance over time.

Unlike traditional automation that follows static rules, agentic AI evolves its approach based on what works best in changing conditions—creating a marketing system that refines its approach with every customer interaction.

The Three Levels of Marketing Execution—And How Agentic AI Benefits Marketers

To understand why agentic AI is so promising, let’s examine the standard approach to marketing execution. Any marketing activity, from its inception and right to post-mortem analysis, goes through three distinct tiers.

At the top, C-level executives set strategic direction—defining brand positioning, growth targets, and overall marketing investment. These decisions require deep business understanding, market intuition, and alignment with broader organizational goals.

In the middle, marketing managers translate these strategic objectives into actionable plans. They determine campaign strategies, audience targeting approaches, channel selection, and resource allocation. This level requires both strategic thinking and tactical expertise.

At the foundation, operational teams bring these plans to life by creating assets, writing copy, designing visuals, and executing campaigns across platforms. This level focuses on skilled execution and creative production.

The first wave of AI primarily impacted the operational level. Generative AI tools now help create content, design visuals, and generate code. This improves productivity but doesn’t change how marketing decisions are made.

Agentic AI is climbing the ladder to support the marketing management layer. Instead of just creating content, these systems can now help determine which channels will perform best, how to optimize campaign timing, when to adjust strategies based on performance, and how to allocate budgets.

It doesn’t mean that marketing will run completely on autopilot—there will still be a need for human oversight, especially at the strategic level, where business context and brand values must guide all decisions. 

However, with AI handling even more tasks, humans will have more space for strategic direction and creative innovation—the things that have the biggest impact.

Conclusion

Agentic AI is what you should keep your eyes on for the next year or two. It’s coming, and it’s going to be big.

And while its technology is fascinating, what matters most to marketing teams are the tangible benefits it delivers. Armed with agentic AI, marketers will be able to launch hyper-personalized campaigns at scale, continuously optimize and test campaigns, effectively allocate budgets, and more.

As miraculous as it may seem, this new type of AI won’t eliminate the need for skilled marketers. This technology works best when given clear objectives and constraints by human experts. 

The most successful implementations will be partnerships between human creativity and strategic thinking combined with AI's analytical power and execution capabilities.

If you’d like to stay updated on the latest news in the world of marketing and AI—be sure to subscribe to our blog. And don’t hesitate to reach out, if you have any questions about the implementation of AI-powered solutions in your own business.

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