Multimodal agents refer to agents that can only process text, but can simultaneously receive and utilize multiple inputs such as images, voices, interface states, documents, and even videos, and then combine them with tool calls and task planning to execute actions. It has been attracting more and more attention recently because many real tasks do not only happen in the text, and for agents to really work, they must first "see, understand, and move".
Why is it harder than a regular chat agent?
- Input is more complex, not just text, but also dealing with visual, speech, and interface context.
- It is easier to disconnect between perception and execution, for example, understanding a page does not mean clicking on a button.
- Once connected to real tools and real environments, the cost of errors will be much higher than text Q&A.
Why does this direction continue to heat up?
| The reason for the heat | Explained |
|---|---|
| GUI Agent is on the rise | More and more systems are trying to get AI to operate computers and web pages |
| Speech and vision models are more mature | The input plane is no longer limited to text |
| Real tasks are more demanding | Businesses and individuals alike are expecting agents to actually perform complex tasks |
The value of a multimodal agent is not in a few more fancy inputs than a chatbot, but in how close it is to the form of real-world tasks. You can understand it as an intermediate step from "talking" to "observing, judging, and acting". It is precisely because it steps on the intersection of vision, speech, tools and task execution that it has become an increasingly hot word.
