How Agentic AI Broke the Rules of Martech Decisioning

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 articles, the history of agentic AI, current considerations for organizations, and quick-win use cases will be explored.

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 frequent adjustments in dynamic marketing, service, and support environments.

When Predictions Stopped at Insight

Machine learning and predictive analytics became popular in the 2010s. Churn was forecasted, leads were scored, and other predictions around purchase or response were created. However, model output scores were fed to humans or BI dashboards, not embedded into workflows. Insight was delivered, but decision or action automation was not yet realized.

The Rise of Robotic Task Workers

Robotic process automation (RPA) emerged in the early 2010s as the first software agents. These low-level “robots” or rule-based processes completed mostly back-office operational tasks such as finance, service, and support. They were not focused on front-end customer experience.

How Chatbots Brought Agents to the Front Line

Conversational AI via chatbots moved agents from back-office to front-office customer experience in the late 2010s. Vendors created predefined conversation flows and narrow NLP for customer service and lead qualification. These conversational systems were often siloed, with limited integration outside dialogue. 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 for over 15 years. These solutions help brands design journeys based on segments, triggers, and channel rules. Initially, these engines relied on static logic and success criteria, lacking real-time adaptability and scalable personalization.

The Evolution That Made Agentic AI Inevitable

Agentic AI is the natural evolution of automation, intelligence, decisioning, and autonomy. It replaces hard-coded rules, passive analytics, and rigid workflows with goal-oriented agents that can reason, act, and learn inside your martech stack. AI agents capable of adaptive decisions and continuous learning are poised to transform martech. They deliver contextually intelligent actions with minimal human intervention and can co-create and optimize customer journeys in real-time based on data and feedback. Editor's note: This is Round 1 of a three-part series. The next post will outline tips and tricks for integrating AI agents into your martech ecosystem.

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 experience in marketing and analytics, he has held roles at Earnix and Teradata Corporation in pre-sales, consulting, and marketing. Source: Originally published at CMSWire on July 25, 2025.

Frequently Asked Questions

What is Agentic AI?

Agentic AI refers to adaptive, goal-driven agents that make decisions based on data analysis and dynamic learning instead of following static rules.

How does Agentic AI differ from traditional systems?

Traditional systems often use hard-coded logic without the capability to adapt in real-time, whereas Agentic AI can learn and make decisions autonomously.

What industries benefit most from Agentic AI?

Industries dependent on dynamic customer interactions—like marketing and customer service—benefit greatly from Agentic AI.

Can Agentic AI operate alongside current martech systems?

Yes, it often integrates into existing martech stacks to enhance decision-making capabilities and automate more processes.

Crypto Market's Take

In the rapidly evolving landscape of AI and blockchain technology, our platform at Crypto Market AI is at the forefront of incorporating cutting-edge AI technologies like Agentic AI into the cryptocurrency market space. By leveraging advanced AI-driven trading tools, we empower investors with automated decision-making capabilities, optimizing trading strategies to respond to real-time market movements. With these technologies, we're reshaping how users engage with cryptocurrency, ensuring smarter and more efficient trades.

More to Read:

• Agentic AI Turns Enterprise Cybersecurity into Machine vs. Machine Battle
• AI-driven Crypto Trading Tools Reshape Market Strategies in 2025
• Neutrinos Releases AI Agent Library to Enhance Martech Ecosystems
• Intuit Adds AI Agents & Other Enhancements to Enterprise Suite