Key Points:
- Agentic AI is a powerful application of AI, one that combines LLMs, RAG (Retrieval-Augmented Generation), and increasingly, LAMs (Large Action Models) at scale and now shows traction in media and entertainment.
- Agentic AI consists of AI agents: one that coordinates tasks, mimicking human decision-making and others that provide data and enable control flows to solve problems in an autonomous way, i.e., without human intervention or continuous supervision.
- Like most AI models, it is best for personalization when combined with human interaction to deliver the best results for viewers.
- Learn how XroadMedia utilizes agentic AI for improved search experiences, personalized recommendations based on social trending topics and more.
Agentic AI consists of sophisticated machine learning models that mimic human decision-making. These “AI agents” are designed to solve problems and independently, perceive their environment, reason about their goals and take action without constant human oversight.
There is talk that agentic AI is the new kid on the block. According to a Google survey, 54% of media and entertainment executives report that their organizations are now actively using AI agents in production, with 40% reporting they have launched more than 10. But how effective are they when it comes to personalization and content discovery?
How does Agentic AI help with workflows and personalization
While traditional recommendation engines offered incremental gains, the emergence of agentic AI represents a paradigm shift, moving from static algorithms to autonomous, goal-oriented decision-making that transforms every viewer touchpoint.
Example 1: Dynamic UI and Content Presentation
Agentic AI revolutionizes the dynamic optimization of a video service’s home screen by establishing an autonomous, closed-loop system for continuous improvement.
- A dedicated data agent seamlessly ingests real-time analytics, making comprehensive user behavior and consumption metrics available to the core agentic platform.
- The system then leverages advanced machine learning to analyze this data, identifying nuanced usage patterns, preference signals, and optimal viewing pathways across granular user cohorts.
- A Large Action Model (LAM) system then takes over the execution phase, autonomously configuring and deploying targeted A/B tests with variations of the home screen interface. Such as different content rails, thumbnail styles, or promotional placements, designed to reflect the identified cohort patterns. This eliminates manual intervention in test creation and deployment.
- The agentic system continuously monitors the performance of these variations against KPIs, automatically adjusting the parameters and ultimately finding the statistically optimal home screen configuration for each distinct cohort, ensuring maximum engagement and conversion across the entire user base.
Example 2: Conversational Search with Agentic AI
Agentic AI also enhances the user experience for content discovery by seamlessly integrating with voice interfaces.
- A user engages the video platform through voice; the system first identifies the language spoken and performs a precise speech-to-text translation.
- A dedicated AI agent then decomposes the resulting text, accurately identifying the user’s underlying intent, key concepts and potential keywords.
- After analysis, the agent autonomously decides the optimal search strategy, for instance, determining if the user is searching based on a detailed plot description, looking for content of a particular mood or genre, or seeking a specific title or actor.
- The agent then selects the right methodology to translate the user intent into a structured query that can be executed against the available content database.
- Relevant results are presented to the user
- If needed, the user can then provide additional input and refinement through subsequent voice commands, creating a dynamic and highly intuitive conversational discovery loop.
Agentic AI systems use components that are not completely real-time, relying on an agent to reason, gather information from other agents and decide which agent to use. It offers automatic “plug and play” functionality by using agents to dynamically resolve needs based on user intent.
Make an Impact with Less Time
Although not real-time, agentic AI can deliver tailored recommendations to enhance your viewers’ experience. One example of how XroadMedia is using agentic AI is delivering recommendations based on social trends. Where an agent captures information from sources like Google Trends, and a core agent educates themself by consulting sources like Wikipedia and the BBC to understand trends, such as “#F1” referring to racing and world championships. Therefore, agents discover racing-related content within a catalog and display it on an existing or dedicated rail for the viewer.
Over time, agents build knowledge and a database used to recall these trends in real time, enabling them to deliver personalized recommendations based on social trends. The agentic AI can be given full control to inspire consumers or allow editors to integrate the suggestions, making it part of an editorial flow. This saves time for the editorial and content teams, who otherwise would have had to manually check social platforms, translate trends to searchable concepts in their catalog, to then crawl through the entire catalog to find relevant content.
Enrich Your Metadata
Good metadata is the foundation for semantic content discovery. With agentic AI, weak metadata can be strengthened, going beyond the simple tagging, but creating more accurate and more dynamic metadata, which is crucial for a better, more natural search, recommendation and personalization. In addition to analyzing text (closed caption subtitles) or the audio directly, agents can check, if there is a license to it, third-party databases such as IMDb. Then solutions like XroadMedia can fill the gaps within the metadata by pulling in the information in our recommendation solution to enrich existing assets.
The 2025 State of Play Survey by Gracenote found streaming viewers now spend an average of 14 minutes searching for something to watch. With better metadata, you can eliminate the choice paralysis and reduce the time that consumers spend searching for something to watch.
Proactive Retention and Churn Prevention
Traditional personalization often reacts to past behavior. Agentic AI can monitor and deploy relevant retention tactics. Agents autonomously decide the best channel, timing, and content for win-back campaigns. According to McKinsey, personalized customer interactions can increase loyalty by 10-30%. When it comes to retention and churn, service providers can be proactive and reactive. Churn prevention is identifying those at risk by monitoring behaviours and communicating offers that will likely grab the attention of your customers. The most effective campaigns are the ones that are tailored to their profiles and preferences. Take a seasonal subscriber, who, during the summer, as there are fewer sports airing, is at risk of cancelling. An agentic AI workflow can monitor consumption patterns to suggest a better-fitting package, such as moving from a sports package to a movie and documentaries package based on consumption. Or even suggest pausing the subscription for a couple of months till the following spots are back with live events, to avoid a highly probable cancellation. Proactive retention involves constantly monitoring user consumption patterns, identifying slowdowns compared to typical users, and then identifying and addressing a gap that would keep them subscribing.
Balancing the Power and Risk
Autonomously understanding deep context and acting on real-time signals, is nothing new. Something XroadMedia’s solution has been doing for years. The change is how agentic AI enables autonomous execution on these insights via LAMs in a very flexible way across various systems. Like with most AI applications, it’s best when agentic AI is combined with human interaction. For example, if an eclipse was happening, so the topic was trending on social media, agentic AI would find a particular episode that features an eclipse in a children’s programme. This might not be relevant to the majority of users. Mitigating risks by setting boundaries for automatic social recommendations, such as no adult or children’s content or limits on PG ratings, to ensure the content stays in a “safe space”. Contributing to building trust with users. When implemented thoughtfully, agentic personalization can empower users, streamline operations, and breathe new life into customer engagement, while requiring ethical guardrails to ensure trust remains at its core.
Personalize and Scale with Agentic AI
Agentic AI enables active understanding and tailoring to user behavior at a cohort and finer-grained level, which is not technically feasible at scale without it. If you want to save time, get more effective, or more personal with your users, get in touch and discover how we’re saving one broadcasting editorial team 3 to 4 hours of work, just with smarter workflows, delivering hyper-personalized experiences.
Speak with the personalization experts today or join our upcoming webinar to hear from Siminn and UKTV on how they have approached their personalization strategies. Register for the webinar on 4th December 2025.
