A recurring theme in this column is that change is the only constant in media technology and now we’re entering yet another inflection point. For the past two or three years, the conversation has been dominated by generative artificial intelligence (GenAI) large-language models, synthetic media and the promise (and peril) of machines that can create. But a new concept is emerging to take center stage: Agentic AI.
This shift is more than just a buzzword swap. It represents new thinking in how we consider automation and interaction. Where GenAI focused on content generation, Agentic AI is about delegation and communication. While the technology is still maturing, the trajectory is clear: agents are coming.
What Is Agentic AI, Really?
Before we go further into the world of agentic systems, it’s worth stepping back to clarify what we mean when we talk about “AI.” AI is not a single technology—it’s a spectrum of capabilities, each suited to different kinds of problems.
Over the past few years, the spotlight has been on generative systems—models that can produce text, images, audio or video based on patterns learned from large datasets. Attention is shifting toward something more dynamic: Agentic AI. These are systems that don’t just respond—they act. They can pursue goals, make decisions and interact with other systems or agents on behalf of a user.
A key distinction between AI and traditional automation lies in determinism: Traditional automation excels in deterministic environments, where inputs and outputs are well-defined and predictable. Think of a transcoding pipeline or a playout automation system. These are engineered for consistency and reliability.
AI, by contrast, thrives in nondeterministic contexts—where inputs may be ambiguous, incomplete or constantly changing, and where outputs are not always binary or fixed. This makes AI especially useful in areas like content personalization, natural language interaction or adaptive media workflows, where flexibility and learning are more valuable than rigid rules.
As we move into the agentic era, this distinction becomes even more important. We’re building systems that can operate in the gray areas, where human judgment used to be the only option. This is a significant evolution in how we think about automation. These agents may be powered by generative models, but they go beyond them by incorporating memory (context), planning and the ability to interact with other systems or agents. In some cases, it may even act without direct prompting based on what it knows about your goals.
The key point is this: Agents are not just tools. They are actors in a system, capable of making decisions, forming strategies and interacting with other agents in ways that mirror human delegation.
A New Media Ecosystem
To understand how agentic systems might reshape the media landscape, it helps to visualize the ecosystem they could create. That’s where the Agentic Model for media comes in.
At the center of the model is “Agentic Discovery and Communication.” This is the core function that ties everything together: the ability of agents to find, filter, personalize and exchange content on behalf of their human or organizational counterparts. This is a foundational concept: the emergence of a general agent communications plane-A layer that sits “above” the internet as we know it today. This plane would allow agents to interact, negotiate and transact with one another directly, without requiring constant human mediation.
Some envision a future where this agentic layer becomes the dominant interface for digital interaction—potentially superseding the traditional web. In such a world, websites and apps may become secondary to the agents that navigate the digital world on our behalf.
Surrounding this core are four key roles:
- A Creator Agent might help manage rights, optimize distribution or assist in content creation or personalization;
- A Brand Agent could autonomously place ads for a brand, negotiate campaign terms or monitor performance;
- A Personal Curator Agent would act on behalf of the consumer, filtering content, managing preferences and even negotiating access or pricing; and
- An Influencer Agent represents any entity granted the authority to shape or guide the behavior of other agents. This could include institutions, communities, regulatory bodies or even parents wishing to influence the curator agents of their children.
What emerges from this model is a vision of a media ecosystem where agents mediate nearly every interaction. It’s a shift from a platform-centric media model to an agent-centric one, where the locus of control moves closer to the individual or organization being represented.
Personal Agents and Data Ownership
Among the most transformative elements of the Agentic Model is the Personal Curator Agent—a digital representative that acts on behalf of an individual consumer. This agent doesn’t just recommend content: it negotiates access, filters noise, adapts to evolving preferences and potentially even manages subscriptions or monetization decisions. It becomes, in effect, a media concierge—one that knows your tastes, your values and your boundaries.
The need for such a capability has never been more urgent. We are rapidly approaching—if not already living in—what some have called the “dead internet,” a digital landscape increasingly saturated with AI-generated content, synthetic engagement and algorithmically amplified noise. In this environment, the content signal-to-noise ratio is getting worse. We will need agents to sift through the junk and identify what truly matters.
For a personal agent to be effective, it must have access to a rich and continuous stream of behavioral, contextual and preference data. That data might come from viewing history, social interactions, biometric signals or even inferred emotional states. In today’s media environment, much of that data is collected and controlled by platforms. But in an agentic future, the balance of power could shift—from platforms to people.
The Long Road Ahead
The pace of technological innovation often outstrips the pace of business model evolution. We’ve seen this before—file-based workflows were technically feasible long before they were widely adopted. Cloud infrastructure was ready years before media companies trusted it with their core operations. Even streaming, now ubiquitous, took more than a decade to become mainstream.
The core technologies—autonomous agents, large-scale models, distributed orchestration—are already emerging. But the real constraint isn’t technical; it’s organizational, economic and cultural. Business models will need to adapt. Rights frameworks will need to evolve. Standards for agent behavior, identity, and trust will need to be developed and adopted. And perhaps most importantly, people will need time to adjust to the idea of delegating meaningful decisions to machines. It’s a decade-long transformation, at minimum.
For media professionals, the message is clear: don’t wait for the future to arrive—start preparing for it now. Begin experimenting with agentic workflows. Rethink how your content is discovered, curated and monetized. Invest in data quality, interoperability and flexible infrastructure. And most importantly, stay curious.
