Agentic AI is being hailed as the future of the MarTech stack—but is it truly transformational or just the latest hype cycle? In this episode, Jarrod Gingras and Tony Byrne break down what agentic AI really is, where it delivers real value today, and why most enterprises are still far from running their stacks autonomously. From task agents to cross-platform orchestration, they share practical use cases, hard lessons from real-world prototypes, and what MarTech leaders should watch out for next.In this episode:What is agentic AI in MarTech?Agentic AI refers to goal-driven, semi-autonomous systems that can plan, adapt, and take action using real-time data—often without human input at every step. In MarTech, agents typically automate tasks, workflows, or cross-platform processes to improve speed, scale, and decision-making.What’s the difference between task agents and orchestration agents?Task or workflow agents operate within a single platform, automating specific functions like data enrichment or campaign setup.Orchestration agents work across multiple platforms, coordinating insights, decisions, and content execution across the MarTech stack.Most vendors today focus on task agents—not true orchestration.Can agentic AI replace core AI services?No. Agentic AI depends on mature core AI services, including:Insights AI (What’s happening and why?)Decisioning AI (What should we do about it?)Generative AI (What content or experiences should we create?)Agents build on these layers—they don’t replace them.What are the biggest risks MarTech leaders should watch for?Agents deployed “willy-nilly” across the stackRising and unpredictable AI processing costsLack of consistency and statefulness across agent stepsOver-reliance on immature AI foundationsPoor customer journey coherence

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