The term "agentic AI" has officially crossed from technical discourse into vendor marketing decks. Platforms like Salesforce and Qualtrics are pushing agent-based intelligence as the next frontier of enterprise automation. Benioff claims Agentforce is resolving 380,000+ help tickets autonomously. Qualtrics is promising orchestrated customer journeys led by intelligent agents.
But while the term sounds futuristic (and let's be honest, budget-worthy), it’s creating more confusion than clarity.
So what exactly are we talking about when we say "agentic AI"?
And how does it actually compare to the structured automation that’s already part of most MarTech stacks?
Let’s unpack it.
What Is Agentic AI?
Agentic AI refers to AI systems that can take goal-directed action based on context. Unlike traditional automation--which follows fixed logic trees--agentic systems can reason, plan, and act semi-autonomously. Think:
- An AI agent that doesn't just recommend next steps, but executes them
- A system that adapts based on evolving user inputs
- A "co-pilot" that goes beyond assisting--it orchestrates
The promise? Fewer humans in the loop. More intelligent workflows. Better outcomes with less manual oversight.
The problem? It’s still incredibly hard to do outside of narrowly defined, tightly scoped environments.
Structured Automation: What Most Companies Actually Use
Structured automation is what powers most enterprise workflows today. These are systems that:
- Follow logic defined in journey builders, rule engines, or workflows
- Require human-defined segmentation, triggers, and content
- Can personalize at scale, but within guardrails
Salesforce Marketing Cloud, Adobe Journey Optimizer, Braze, Iterable, and other common platforms are great at structured automation. They can:
- Send the right message to the right person based on behavior
- Trigger re-engagement flows
- Deliver personalization via content blocks or recommendations
But these systems are reactive, not proactive. And they rely heavily on humans to define and optimize the experience.
Where Agentic AI Is Actually Working Today
The places where agentic AI is gaining traction share three characteristics:
- Narrow domain
- Clear success metrics
- Low business risk if it fails
Examples:
- Customer service chatbots that can resolve billing issues or reschedule appointments without escalation
- Internal knowledgebase agents that can find policy documents and summarize answers
- AI-generated summaries in CRM tools like Gong, Salesforce, or HubSpot
- Cold lead SMS agents built with HighLevel + ChatGPT that carry on structured, semi-autonomous conversations with prospects and book meetings -- especially useful in B2C and small business use cases where fast, persistent follow-up matters more than nuance.
Even Benioff’s Agentforce--for all its marketing swagger--is working because it’s operating in a narrow, structured support domain with defined resolution rules and tight escalation paths.
These are not "do-everything" agents. They’re well-trained interns that follow scripts, learn fast, and know when to ask for help.
Where the Hype Outpaces the Results
Take the now-infamous case of Chevrolet of Watsonville’s website chatbot. In late 2023, screenshots went viral showing the AI agent—powered by Fullpath’s GPT-4-based dealership integration—agreeing to sell a new Chevy Tahoe for $1 and recommending a Ford F-150 over its own inventory. Users were also able to jailbreak it into solving advanced fluid dynamics problems in Python.
The story, first flagged on Mastodon and later picked up by outlets like Inc. and The Detroit Free-Press, highlights what happens when agentic systems are deployed without strict guardrails. While it made headlines for humor, it underscores a serious point: without role restrictions, domain limits, and oversight, agentic AI can behave in ways that feel “smart” but create real risk.
So What Should Marketers Do Now?
Here’s the pragmatic view:
- Don’t wait for full autonomy. Focus on augmentation.
- Look for AI features embedded in tools you already use (Einstein, Generative Content, predictive scoring)
- Identify tasks where automation saves time but retains oversight (summarization, optimization, reactivation)
- Invest in AI readiness: clean data, consent strategy, content modularity, test-and-learn culture
The goal isn’t to hand over the wheel. It’s to make your team faster, smarter, and more responsive--today.
Final Thought
Agentic AI has potential. But most of what’s working in enterprise marketing right now is still structured, supervised, and human-enabled.
The danger isn’t missing the agentic wave. It’s mistaking marketing hype for operational reality.
Smart teams aren’t trying to leapfrog the maturity curve. They’re building trust, use case by use case.
More on that soon.
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📍 Read more at mikehotz.com
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