AI Agents are at least proficient in these four types of work.
Written by: Daniel Barabander, Variant
Compiled by: Luffy, Foresight News
What exactly are the highly popular AI Agents good at? In response to this question, we conducted internal discussions and reached at least four conclusions:
Interacting with humans within applications
Assisting humans in their work
Aggregating and organizing information
Providing entertainment
First, interacting with humans within applications. AI Agents can process human language, so any application that a human can use can theoretically also have AI Agents as users. However, unlike human users, agents can provide services to human users on these platforms at scale.
Thus, agents can act as a layer on top of existing applications that users already like, thereby expanding their utility. For example, with the Bounty Bot on Farcaster, users can post bounties externally, but this adds friction.
By interacting with users, AI Agents can offer convenience, practicality, and ways to derive value from existing applications. But note: not all applications are created to support AI Agents; those with unfixable APIs, such as Farcaster, are the most suitable.
I have written a paper on the main legal issues surrounding agents on Web2 platforms. My research indicates that if users have complete control over the agents, and Web2 platforms attempt to block the agents, users will have to stop running the agents. My conclusion is that agents should be built on open platforms like Farcaster, which is another reason I am particularly interested in agents on Farcaster.
Second, assisting humans in their work. Humans are good at signaling but poor at execution. Agents bridge this gap by doing the heavy lifting while humans guide the outcomes through their preferences.
A good example is BottoDAO. The art it creates is influenced by the input of DAO token holders. The AI handles the hard work of creating art, but humans guide its creative direction through their voting preferences on the artworks.
Third, aggregating and organizing information. Agents can handle vast amounts of data, far beyond human capabilities. For instance, trading bots analyze large volumes of on-chain data to make decisions.
Finally, providing entertainment. This may be the most attention-grabbing category of agents in the crypto space, such as Truth Terminal.
Of course, much of the entertainment value of social agents comes from the novelty of robot-generated content. However, I am more interested in robots generating entertaining content based on their own characteristics, such as interacting with other users on the platform in an engaging way like KOLs.
The advantage of agents acting as KOLs is that once they have a fixed audience, they can easily provide other services, especially those that can generate revenue for the agents more directly than advertising.
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