How Agentic AI Broke the Rules of Martech Decisioning

By Jonathan Moran — July 25, 2025 Hard-coded logic and passive analytics are out. Adaptive, goal-driven agents are the next evolution in marketing technology.

The Gist

• Old rules break. Legacy rules-based systems lacked learning or adaptation, limiting decisioning in real-time marketing.
• Analytics plateaued. Predictive models provided insight but stopped short of automation or workflow integration.
• Agents emerge. Agentic AI builds on past tech by supporting goal-oriented, adaptive decisions inside the martech stack.
Agentic AI has suddenly appeared on the martech scene, leaving some to wonder how to get up to speed on the technology and its uses. Over a series of three short bylines, I’ll dive into the history of agentic AI, how we got to where we are today, the considerations for organizations as they evaluate integrating agentic AI into their martech ecosystems, and what quick-win use cases they can consider undertaking.

When Rules-Based Systems Ruled Martech

Static enterprise decisioning (also known as rules-based decisioning) entered the scene early. It was all hard-coded logic to automate emails, nurture decision paths, or score leads. There was no learning and no adaptation. The created rules needed to be adjusted frequently in dynamic marketing, service, and support environments.

When Predictions Stopped at Insight

Machine learning and predictive analytics — though first introduced in the 1950s — became most popular in the 2010s. Churn was forecasted, leads were scored, and other predictions around purchase or response were created. Model output scores were fed to humans or into BI dashboards, not embedded into workflows of any type. Insight, yes. But decision or action automation, not yet.

The Rise of Robotic Task Workers

Robotic process automation (RPA) was the first foray into software agents in the early 2010s, with vendors offering low-level “robots” or rule-based processes designed to complete mostly low-level, back-office operational tasks. These tasks were responsible for finance, service, and support operations, and they were not focused on the front-end customer experience.

How Chatbots Brought Agents to the Front Line

Conversational AI via chatbots allowed agents to leave the back-office and make their way to the front-office customer experience realm in the late 2010s. Vendors created predefined conversation flows and narrow NLP for customer service and lead qualification. Often these conversational systems were siloed, and outside conversation or dialogue integration was limited. Related Article: The Evolution of AI Chatbots: Past, Present and Future

Orchestrating Customer Journeys by Script

Orchestration engines have supported customer journey orchestration and optimization over the last 15 years. These solutions help brands design customer journeys based on segments, triggers, and channel rules. Initially, these engines relied on standard logic and success criteria. They lacked real-time adaptability and the ability to scale personalization efforts.

The Evolution That Made Agentic AI Inevitable

While many recent solutions, particularly conversational AI tools and orchestration engines, have begun to incorporate agentic AI, it’s clear that agentic AI and decisioning is a natural evolution of this somewhat linear flow. Agentic AI is the natural evolution of automation, intelligence, decisioning, and autonomy. It replaces the hard-coded rules of static enterprise decisioning, the passive analytics of pure predictive analytics and ML, and the rigid workflows of RPA with goal-oriented agents that can reason, act, and learn inside your martech stack. AI agents that can make adaptive decisions and learn over time are poised to change the game in martech. They can deliver contextually intelligent and aware actions or activations with limited human intervention. Agents that will be able to co-create and continuously optimize journeys based on real-time data and feedback are the future. Editor's note: This was Round 1 of a three-part series. The next post in this series will outline several tips and tricks to consider as you start to add AI agents into your martech ecosystem.

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About the Author

Jonathan Moran is Head of MarTech Solutions Marketing at SAS, focusing on customer experience and marketing technologies. With over 20 years of marketing and analytics experience, he has worked at Earnix and Teradata Corporation in pre-sales, consulting, and marketing roles.

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Source: How Agentic AI Broke the Rules of Martech Decisioning — Published July 25, 2025

Frequently Asked Questions

What is Agentic AI?

Agentic AI refers to adaptive, goal-driven agents integrated into the martech stack that can reason, act, and learn, thereby improving real-time decision-making and personalization in marketing.

Why are traditional rules-based systems considered outdated in martech?

Traditional rules-based systems are considered outdated because they rely on hard-coded logic that lacks learning and adaptation, making them less effective in dynamic environments needing real-time decisions.

How does Agentic AI differ from predictive analytics?

While predictive analytics offers insights based on data, Agentic AI goes a step further by enabling the automation of decisions and actions, not just providing insights.

Crypto Market's Take

The rise of Agentic AI in martech echoes trends we see in crypto trading technology, where adaptive AI agents are used to optimize trading strategies and investment decisions. Platforms like AI Crypto Market offer similar innovations in the cryptocurrency space, providing AI-powered trading bots that offer real-time market insights and automated execution.

More to Read:

• The Evolution of AI Chatbots: Past, Present and Future
• AI Agents for Trading: Pros and Cons
• Magentic Raises $5 Million for AI Agents