Agentic AI is shaping up to be the talk of 2025. Is it a game-changer that integrates all your video management workflows, or a hype that will tone down with time? To better understand what AI agents are and how they can be used, we outline what heavy-lifting this technology is actually capable of in the near future.

The Rising Star of AI Agents

Consider a souped-up Tickle me Elmo toy for instance, that can talk to you and offer guidance, perhaps on how to make your coffee. While Large Language Models (LLMs) can already achieve this level of interaction, AI agents go further. By giving these models the ability to interact with the real world, such as a robotic arm connected to your coffee machine, they gain the capacity for autonomous action, for instance preparing your coffee directly. This move from conversation to action defines the core functionality of agents. Whether it’s in robotics, to book a hotel room or to manage your video libraries, the same principle applies. The magic lies in the tools and the data your agent has access to.

While generative AI set the world on fire for its ability to generate new content such as text, images, and code in response to prompts, agentic AI can use this generated content, along with the ability to call upon external tools and resources, to complete complex tasks without supervision. This difference means that while generative AI excels at content creation, agentic AI focuses on the intelligent and autonomous processing and use of information to achieve specific goals, which makes it particularly relevant for applications like video content management and discovery. 

A Great AI Agent Starts With Great Data

The core capabilities of agentic AI, such as its autonomy, smart reasoning, and task-driven nature, hold incredible potential for changing the way we interact with video. 

"Agentic AI is set to redefine the way we interact with machines” explains Dr. Yannis Tevissen, Head of Science at Moments Lab. “Seeing LLM’s fast progress towards better tool-use and compute efficiency, agents are likely to become the default interface, even for complex tasks such as video library management.”

However there’s a catch: the quality of the output is directly proportional to the quality of the input data. 

Traditional indexing assumes that the researcher knows how a video was tagged, if it was tagged at all. Many editors and producers still rely on their team’s memory to find the content they need, like recalling the best shots from a show or finding particular information from an interview. This very manual process slows down production, adds cost, and limits creativity. 

On the other hand, multimodal AI-powered video indexing, such as the one provided by MXT-2, transforms and accelerates the process. By analyzing a video, breaking it down into meaningful scenes, recognizing who’s in them, what’s happening, where it’s taking place, and even what kind of shots are used, then feeding the rich metadata and timecoded human-like descriptions into the AI agent, it becomes an expert on your media library. The agent is capable of conversing with you and answering natural-language queries using contextual understanding and supplementing this with internet searches where needed.

A New Way of Interacting With Media Libraries

Building on AI-powered video-understanding and indexing, AI agents can dramatically improve the way we manage, search for, repurpose and monetize video content. When AI automatically sorts, describes and manages your media library, you can just type in what you want to find and retrieve it in seconds. It's like having a personal AI media researcher that knows exactly where everything is, and that you can chat with at any time.

But the benefits go further. Once the agent understands the video's context and key moments through indexing, it can automatically create short summaries, suggest compelling highlight reels, and even find the right clips for different social media platforms or specific audiences. This drastically speeds up content repurposing, making the adaptation of videos for different uses smarter and faster, so you can reach specific viewers, or quickly react to trending news.

What’s The Best Strategy For Working With AI Agents? 

So how do we most effectively use AI agents in our everyday work? If editors don't know how to give the agent clear instructions (like a good prompt), even the smartest AI agent won't be very helpful. Think about how companies sell their online software (SaaS). It’s changing rapidly because AI agents are starting to take care of some of the tasks we used to do directly with the software.

“Imagine tools you already use, like Slack or Teams. They're like a secret way to bring these AI agents into our regular work. Instead of opening a program and clicking around, you might start by talking to an AI agent within Slack or Teams to get simple things done. The old way of using the software becomes the last step, not the first” explains Fred Petitpont, CTO and co-founder of Moments Lab.

But it's not just about us humans talking to AI. AI agents can also talk to other computer systems. They can quickly read the instructions for how different software works (that's the API) and then write their own code to make those systems talk to each other. It's like they can understand the instruction manuals and then build the connections themselves.

New technologies are also letting AI agents talk to each other in plain language, just like humans do. There's that famous demo where two AI agents worked together to book a hotel room for someone. Even though computers usually speak in code, human language is a really good and flexible way for even AI systems to understand and communicate with each other.

Agentic AI in video discovery and management has exciting potential, however it’s important to understand what is realistically achievable in 2025 and what remains science fiction. That’s right, current AI technology, while impressive, still has limitations, particularly in areas that demand creativity, nuanced understanding of human emotions and intentions, and complex, abstract reasoning. AI agents can very accurately analyze, reason about, and act upon video content, but they are only as good as the quality of the underlying metadata. 

So before we welcome AI agents in every media library, we have some preparation to do. How well indexed is your audiovisual content?

We can help with that. Find out what MXT-2 can do or get in touch with us.