AI Moves Towards Individualized Interaction
Artificial Intelligence (AI) is making significant strides, particularly in nondeterministic contexts where inputs may be ambiguous, incomplete, or constantly changing. This evolution is paving the way for a new concept in AI: Agentic AI.
Agentic AI systems, unlike traditional AI, are proactive agents that can pursue defined goals, make decisions, and interact with other systems or agents on behalf of a user. They are not just tools but actors in a system, capable of making decisions, forming strategies, and interacting with other agents in ways that mirror human delegation.
In contrast, Generative AI primarily creates content such as text, images, audio, or code based on learned patterns from data but does not inherently take autonomous actions or manage complex tasks to achieve broader objectives.
The Differences in Capabilities
| Capability | Agentic AI | Generative AI | |------------|------------|---------------| | Autonomy | Operates independently, initiates and adapts actions toward goals without step-by-step instructions | Produces outputs based on user prompts or inputs but lacks goal-driven autonomous behavior | | Decision-making | Makes real-time complex decisions, weighing trade-offs and adjusting strategies dynamically | Does not make autonomous decisions; generates content passively according to prompt input | | Adaptability | Continuously learns from feedback and adapts strategies autonomously | Typically static in generation; adaptation requires retraining or extending model parameters | | Workflow orchestration | Coordinates multi-step processes across platforms and teams, executing interconnected tasks | Focused on creating discrete content pieces, not process management | | Goal-orientation | Pursues explicit objectives, adjusts behaviors to achieve outcomes proactively | Responds to isolated prompts, without overarching goals |
Applications in the Media Industry
The Agentic AI revolution is poised to transform the media landscape. In the proposed media ecosystem, websites and apps may become secondary to agents navigating the digital world on behalf of users.
One key role in the Agentic Model for media is the Personal Curator Agent, acting on behalf of individual consumers. This agent negotiates access, filters content, adapts to evolving preferences, and potentially manages subscriptions or monetization decisions. For it to be effective, it requires access to a rich and continuous stream of behavioral, contextual, and preference data, which currently is often controlled by platforms.
The shift in power from platforms to people is a potential outcome in an agentic future.
Agentic AI can revolutionize media operations by autonomously managing content pipelines, coordinating scheduling, optimizing resource allocation, and making editorial decisions based on audience analytics in real time. It could adapt campaigns, personalize experiences proactively, and streamline complex workflows between creative, production, and distribution teams.
While Generative AI supports creative content production, Agentic AI functions as an intelligent orchestrator and decision-maker that can manage entire processes autonomously, enhancing efficiency and responsiveness in operations and strategic initiatives.
For instance, an agentic AI system could autonomously detect trending topics, generate related content, schedule releases, coordinate cross-platform marketing, and optimize audience engagement strategies without manual oversight. Generative AI, meanwhile, would focus on creating individual pieces of text or visuals when requested.
In summary, Agentic AI combines generation, reasoning, action, and adaptation, making it a transformative technology for complex and goal-driven applications in the media industry, beyond the content-centric role of Generative AI.
Media professionals are advised to start preparing for the agentic future now, by experimenting with agentic workflows, rethinking content discovery, curation, and monetization, investing in data quality, interoperability, and flexible infrastructure, and staying curious. The pace of technological innovation often outstrips the pace of business model evolution, and the transition to an agentic media model is expected to take at least a decade.
The Agentic Model for media proposes a core function of Agentic Discovery and Communication, enabling agents to find, filter, personalize, and exchange content. It also suggests the emergence of a general agent communications plane, a layer that sits above the internet, allowing agents to interact, negotiate, and transact directly. This includes four key roles: Creator Agent, Brand Agent, Personal Curator Agent, and Influencer Agent.
The need for Personal Curator Agents arises from the increasing saturation of AI-generated content, synthetic engagement, and algorithmically amplified noise in the digital landscape. The Agentic Model could potentially reshape the media landscape by creating a new ecosystem where agents can manage content discovery, filtering, personalization, and exchange.
- In the realm of Agentic AI, these systems not only create content such as audio, video, or text, but they also operate independently, initiating actions towards defined goals without needing step-by-step instructions.
- The Agentic AI revolution could redefine the media landscape, causing websites and apps to become overshadowed by agents navigating the digital world on behalf of users.
- In the proposed Agentic Model for media, a Personal Curator Agent acts on behalf of individual consumers, negotiating access, filtering content, adapting to evolving preferences, and managing subscriptions or monetization decisions.
- Agentic AI could revolutionize media operations by autonomously managing content pipelines, coordinating scheduling, optimizing resource allocation, and making editorial decisions based on audience analytics in real time.
- While Generative AI primarily generates content, Agentic AI functions as an intelligent orchestrator and decision-maker, managing entire processes autonomously and enhancing efficiency and responsiveness in operations.
- In the agentic future, there is a potential shift in power from platforms to people, as Personal Curator Agents could handle content discovery, curation, and monetization decisions.
- The Agentic Model proposes a core function of Agentic Discovery and Communication, enabling agents to find, filter, personalize, and exchange content directly, suggesting a potential emergence of a general agent communications plane.
- The increasing saturation of AI-generated content, synthetic engagement, and algorithmically amplified noise in the digital landscape necessitates the development of Personal Curator Agents under the Agentic Model, which could potentially reshape the media landscape by creating a new ecosystem where agents can manage content discovery, filtering, personalization, and exchange.
