From prompting to managing: The rise of agentic marketing

Shelly Palmer is the advanced media professor in residence at Syracuse University’s Newhouse School of Public Communications and CEO of The Palmer Group, a consultancy that helps Fortune 500 companies with technology, media, and marketing. The views expressed in this perspective are those of the author and do not necessarily reflect the views of Google.

Call it prompt engineering, context crafting, or one of a dozen other names. The goal is the same: get the most out of AI by treating your words as the new programming language. Every marketer needs to be great at this. It’s the new thing, but the tech is changing at an almost incomprehensible pace.

As a marketing leader, you have a new AI strategy mandate: leverage your team’s prompting skills to build the hybrid human-agent teams that will ultimately evolve into a super-productive autonomous workforce. The era of the tool operator is ending. We’re in the age of agent managers. It’s the dawn of the agentic frontier.

The strategic distinction: prompts vs. agents

While prompt engineering is a useful tactic, marketers need to shift to a broader strategy: agentic marketing. This pivot enables teams to manage autonomous AI agents to achieve business goals. Moving from instruction to delegation takes an unprecedented operational shift, and it’s going to be an exceptional challenge for leaders.

CMOs need to move from a linear content supply chain, where humans touch every asset, to an agentic model, where humans set the “brand bible” and agents generate the thousands of necessary adaptations for social, display, and search.

The technical line between a complex prompt and a simple agent is nuanced. The strategic distinction, however, is clear. A prompt is a command given to a tool; the human provides the reasoning. An AI agent is an autonomous system that independently plans, executes, and iterates to achieve a goal.

Said differently, an agent is a thing that does a thing, and an agentic system is a group of agents orchestrated to accomplish a larger goal. Tool choices must be governed by clearly defined outcomes, not the underlying mechanics.

A leadership framework for hybrid human-agent workflows

The challenge of deploying AI agents is operational, not technical. It starts by deconstructing workflows into discrete tasks. These must be analyzed and well understood. The best practice is to go back to first principles to identify which discrete tasks would still be required if the process was partially (or fully) automated.

In practice, agents deployed today require continuous human monitoring and control. This reality necessitates a robust governance framework before any agent is given meaningful responsibility. Agentic systems require the same rigor as enterprise IT. Governance must precede scale. Foundational controls include:

• Agent permissions tables to define and limit system access
• Agent life cycle management to track creation, deployment, and retirement
• Clearly defined guardrails to ensure agents operate within grounded boundaries and regulate which AI models are approved for use

CMOs can apply this immediately to processes like the quarterly competitive audit. Instead of burning junior hours on manual scraping and screenshotting, an agent can autonomously monitor competitors 24/7 and synthesize anomalies. This shifts your team from “data gatherers” to strategists focused entirely on countering market moves.

Executive implications

This transition from tool operator to agent manager has immediate consequences for talent, operations, and budget. First, the skill set of a marketing team must evolve from execution to governance. Marketers will define the goals and constraints for AI agents. Ultimately evolving away from executional task management and optimization into roles that focus on strategy and aesthetic judgment.

Second, agentic marketing requires a new operating model. Given the level of autonomy that makes AI agents valuable, we must govern the tech with the same rigor as human teams. Leaders should resist the urge to anthropomorphize these systems while still managing them like new hires, granting role-based access, defining clear boundaries for autonomous decision-making, and enforcing controls that limit operational and financial risk.

Action plan

Waiting to develop an agentic AI strategy introduces competitive risk. It’s time for a 90-day audit of your current tech stack for agent readiness. The audit should identify which platforms offer access for control and data integration. The outcome will be a gap analysis and a road map for building a tech stack that an autonomous marketing workforce can operate.

Google Cloud’s The AI agent handbook (October 2025) outlines the technical foundation for this transition, including how to deploy autonomous agents on Vertex AI Agent Builder. It’s a practical starting point for building your outcomes-focused agentic marketing strategy.