“Agentic AI” was 2025’s loudest buzzy phrase and, confusingly, a term that a lot of people had trouble explaining. The hard part is telling substantive, no-BS capability from shiny relabeling.
Let’s start with what most experts agree on: Agents aren’t a single thing, they live on a spectrum. Toward the middle of that spectrum are task agents that plan multi-step work and “call” third-party tools; at the far end are semi-autonomous “virtual teammates” that pursue goals, escalate when uncertain, and improve with feedback.
But let’s keep things fairly simple: Agentic AI is software that owns a goal over time and can act toward it without you babysitting every step. It plans its own sub-tasks, uses tools/APIs, and remembers or learns from outcomes to adjust the next move.
“Agentic AI has the agency to operate autonomously, make decisions, and adapt in complex marketing or general environments with minimal human oversight.”
--Eric Walzthöny Kreutzberg, Co-founder and CTO, SmartAssets
The key is autonomy plus feedback loops. Truly agentic AI proposes, executes, checks results, and iterates.
Agents shrink the time and distance between insight and action, and they increase efficiency so that your human teammates can focus on the stuff the machines can’t. In practice, they're getting good at breaking down messy marketing tasks into steps, calling the right tools (search, analytics, asset libraries, CMS), and documenting their decisions.
Agents can maintain memory recall across marketing workflows, respect constraints you set (budgets, brand rules, approvals), and run continuous, tiny experiments to improve their results.
Here are a few concrete examples of what a marketing-focused AI agent could do:
A big reason agentic AI is improving fast involves training in simulated “environments,” where agents practice multi-step software use with rewards and penalties. Silicon Valley is pouring money into these environments to make agents more reliable.
Plenty remains overpromised (it’s why critics have flagged “agent washing” as an ongoing problem). When it comes to things like long-term judgments or predictions, or finessing small cultural nuances, marketers still need a human touch.
Integration also needs to improve if marketers want robust execution. That’s why having a centralized agentic AI experience like the one offered by The Marketing Cloud is important, especially if you need a data-secure environment rather than a sloppy, Bring-Your-Own-AI mess.
Compliance is another hurdle. Marketing in regulated categories (pharmaceuticals, finance) still demands human checkpoints because agents will occasionally overstate a claim or cite the wrong basis. Brands and agencies have to weigh the gains in productivity and efficiency with their own risk appetite, putting guardrails in place sooner rather than later.
That said, agentic AI isn’t just hype, as evidenced by Google’s comprehensive report, “The ROI of AI 2025.” AI agents are swiftly moving into the enterprise core, with potential buyers citing data privacy as their top concern, followed by ease of integration and cost.
While we’re sure to see ongoing splashy promises and spectacle, the real agentic AI action in 2026 will involve the more mundane work of getting these systems up and running in a way that balances autonomy with risk.
Marketers kicked off this past year with agentic AI front-and-center in the conversation at CES 2025 in Las Vegas, where keynotes leaned hard into “agentic AI”—Nvidia literally framed it as the next platform moment. The mood back then was bullish, but now that the hype is settling, it’s time to focus on governance, results, and ROI.
That means building systems that are trustworthy, secure, and results-driven. It’s why exploring the potentials of custom GPTs and AI agents within an environment like Agent Cloud can be so helpful, especially for brands or agencies that don’t have an existing AI infrastructure in place.
