The rise of Agentic AI: What smart marketers need to know | Simon AI

The rise of Agentic AI: What smart marketers need to know

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Author
Ed Walloga
eCommerce Executive & Guest Contributor

Table of contents

It seems like only yesterday that AI burst on the scene in a flurry of buzzwords. Suddenly, every predictive model and machine learning algorithm was being rebranded as "AI."

And that was just the beginning. As AI took center stage, expectations surged. Marketers found themselves sorting through a wave of new terms, tools, and claims, without a clear sense of what AI could actually solve beyond writing copy. Could it help with the complex data and workflow challenges at the heart of their biggest execution problems?

The latest development, however, presents an opportunity to change that. "Agentic AI" is the latest step in the rapid evolution of practical AI. It creates a framework by which various AI solutions can work together to do complex work and achieve results, not just copy or recommendations.

To help marketers capitalize on this inflection point, we’ll demystify Agentic AI, explaining not just what it is, but also how it can unlock new possibilities in CRM and one-to-one marketing.

A quick primer on AI in marketing

To understand Agentic AI, it’s helpful to view it as part of a multi-phase evolution…one that began several years ago.

Phase 1: Predictive AI (machine learning → deep learning)

The foundation of modern customer analytics, this is where AI first gained real traction about a decade ago. It evolved from manually built algorithms using structured data into Deep Learning systems that could draw from much larger datasets, both structured and unstructured, delivering more accurate predictions without the need for step-by-step programming.

Focus: Forecasting future customer behaviors based on historic data
Under the Hood: Regression models, decision trees, clustering, neural networks
Common Use Cases:

While powerful, Predictive AI often required data science resources to build, implement, and optimize. Marketers could leverage the results, but couldn’t improve or expand them without help.

Phase 2: Expressive AI (natural language processing + generative AI)

This is what unlocked AI for the masses. In just the past three years tools like ChatGPT, Gemini, and Claude let non-technical users tap complex algorithms with simple chat-like prompts. The result was the ability to generate copy, images, and plans with a fraction of the time and resources previously required.

Focus: Receiving natural language inputs and generating content (text or images) in response
Under the Hood: Large Language Models (LLMs), Natural Language Processing (NLP), text-to-image, audio & video generation
Common Use Cases:

This phase democratized AI and opened the sandbox to folks without a data science degree. But it still only accelerated tasks and required human prompts to drive every output.

Phase 3: Agentic AI (autonomous agents using predictive + expressive AI)

The newest development in AI’s evolution promises to supercharge what has come before. Agentic AI combines predictive logic with expressive capabilities and adds autonomous decision-making.

Focus: Performing multi-step processes to reach user-defined goals ‍
Under the Hood: AI “agents” with memory, planning, and self-correction capabilities, along with goal-based orientation
Common Use Cases:

This is a natural progression: from insight to creation to orchestration. And marks a major turning point in speed, scale, and customization.

The AI Agents: Meet your virtual team

At its core, Agentic AI introduces a new actor into the marketing tech stack: the AI Agent.

An AI agent is a software system that uses artificial intelligence to perform tasks and achieve goals with a degree of autonomy. It’s more than a simple chatbot: it can plan, adjust, and even interact with other tools and systems. Goal orientation is the critical distinction from the narrow, task-based completion of earlier phases. For example, Generative AI can write the marketing copy, but Agentic AI can define and run the entire campaign.

Think of these agents as your virtual team, each focusing on a different skillset to achieve your overall objective. And like all good teams, you don't need to tell them what to do every step of the way.

With that metaphor firmly in mind, let’s take a look at the types of agents that are emerging as key members of the AI-powered team.

Agents Description
Insights Agents Gather and organize signals from all channels and data sources to surface key trends. Sources can include first-party structured data (sales history, campaign performance, site and app engagement), first-party unstructured (a brand’s social media comments, product reviews, gift messaging, customer care chats) and third-party (weather info, local reviews, broad social media trends, published demographic insights).
Data or Segmentation Agents Leverage insights to build and refine audiences, and recommend new segmentation and personalization opportunities. They can even construct new variables to improve targeting, and automatically integrate them back into your data structure.
Content Agents Develop personalized creative for use in emails, ads, and messaging across channels, using Generative AI to design custom copy and imagery to be pulled into channel-specific templates. Without bandwidth limitations, they can do this across a multitude of segments and micro-segments.
Automation or Journey Agents Pull it all together, triggering a series of touchpoints at the user level, optimized for timing and content, to drive the defined goal (conversion, clickthrough, survey response). They can do this uniquely for each and every customer with no scaling issues.
Optimization Agents Maximize performance of those touchpoints by tuning and rebalancing campaigns in real time. They score audiences against defined goals, forecast conversion and revenue impact, and recommend adjustments to improve targeting and allocation. The result is continuous optimization that ensures the highest-yield opportunities always get priority.

Critical to the Agentic AI concept, these agents can even collaborate with each other, passing data, syncing learnings, and adjusting strategies.

See how Data Agents work inside Simon AI

Simon AI Data Agent: Unlock hidden signals for smarter marketing - YouTube

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Why it matters: From bottlenecks to scaled customization

If you’ve ever waited three weeks for a data request or had creative bandwidth stall a campaign idea, you’ll understand the potential of Agentic AI. It removes bandwidth and timelines as gating factors—letting programs and tests go to market faster.

Insights and Data agents mean you no longer need to wait in prioritization queues to understand segmentation opportunities or establish a new targeting dataset. Content agents enable scaled creative development cycles without the additional cost of an expanded creative team or a massive freelancer budget. Automation agents working with Optimization agents allow you to deploy, test and iterate dozens of campaigns each week, without overloading your team.

As a result, marketers shift from “task executors” to “strategy drivers”. AI handles the time-consuming operations, freeing teams to develop bold ideas and richer customer experiences.

And just as importantly, it unlocks a true 1:1 customer experience, enabling micro-segments and journey moments that have always been on the wishlist but were too resource-intensive to scale. Now Agentic AI can handle that scale, allowing you to drive customer connections in ways that blast campaigns never could.

What to look for in an AI platform

Since the AI space is evolving by the day, best practices are still being written. One thing is certain, though: the foundation is data. Customer data, campaign data, website data, customer care logs, social trends, weather forecasts, regional events…all fuel the agents that craft targeted campaigns and timely touchpoints, turning engagement moments into opportunities to delight.

That makes choosing the right AI Platform to house, process, and act on that data critical to realizing the full potential of this new frontier. In many ways, this is the natural next step in the evolution of the Customer Data Platform.

With that in mind, here is a checklist to guide your selection:

  1. Data Integration
    • How easily does the platform work with modern data warehouses like Snowflake and Databricks?
    • Can it handle both structured and unstructured data?
  2. Agent Capabilities
    • Does it offer a full suite of agents? Beyond analytics and copy generation, look for platforms that support planning, optimization, and orchestration.
  3. User Interface
    • Does it enable conversational and intuitive interactions? You shouldn’t need a computer science degree to guide and interact with the program.
  4. Human-in-the-loop Control
    • Can you review and approve recommendations before launch?
    • Is it easy to provide feedback and adjust behavior?
  5. Governance and Security
    • Are outputs trackable?
    • Does the platform offer role-based access, compliance support, and audit trails?

Your CDP sits at the heart of Agentic AI activation. Agents can only perform as well as the data they operate on. The CDP provides the foundational dataset, and it’s increasingly where agents live and operate. The right CDP becomes not just a warehouse, but a launchpad for intelligent action.

What marketers should do next

If the first step is to understand the possibilities (and hopefully we’ve helped with that), then the next step is to explore those possibilities:

1. Speak with your CDP or MarTech lead

Explore what agentic capabilities already exist in your stack. If none are available internally, then speak with CDPs and platforms that already have capabilities in market and explore a potential fit.

2. Start with one Use Case

Pick a goal or program that’s been on the roadmap for a while, but has been hampered by lack of resources or limited scale: a microsegment, a reactivation journey, a location-based recommendation series. These will serve as real-world use cases to see what your Agentic AI can do. ‍

3. Focus on Human-in-the-Loop Workflows

Begin with semi-autonomous agents where you review and approve recommendations. Build personal trust and enterprise confidence in AI capabilities and the potential upside.

4. Be Ready to Course Correct

As with any new collaboration, there will be growing pains. That is part of the process. By adjusting and trying again, you’ll improve the Agents’ performance, as well as your own proficiency in leveraging them.

5. Shift Your Mindset

Think of Agentic AI not as a tool, but as a collaborator. The goal is to give it the right guidance and feedback to let it run and ultimately scale your impact across multiple programs.

The future is now

Agentic AI is here. For marketers and CRM leaders, that’s both daunting and exciting. With clarity about what agents do and how they can help you, you’ll find an opportunity to solve real problems: faster segmentation, smarter campaigns, true 1:1 personalization at scale. It’s about shifting from repetitive execution to bold, strategic direction and a more expansive customer experience.

Is the technology evolving quickly? Absolutely. The technology shift has led to very rapid innovation in how marketing products are being designed today. Broad usage and intense focus are improving performance at a breakneck pace. And as these agents become more capable, the marketers who thrive will be those who lean in early, experiment often, and deploy these new “teammates” with clarity and creativity, unlocking myriad customer moments that feel both personal and limitless. Now is the time to start taking action to learn and choose use cases where Agentic AI can help you. In six months, the landscape will be different, and the brands that make moves now will be ahead of the curve.