AI Agents are at least good at these four types of work.
Written by: Daniel Barabander, Variant
Compiled by: Luffy, Foresight News
What exactly are the rising AI Agents good at? We conducted internal discussions on this question and reached at least four conclusions:
Interact with humans within applications
Helping humans work
Aggregating and organizing information
Entertainment value
First, in applications interacting with humans. AI Agents can handle human language, so any application that a human can use, AI Agents can theoretically also become users. However, unlike human users, agents can serve human users on these platforms at scale.
Therefore, agents can act as a top layer over existing applications that users already like, thus expanding their utility. Take the Bounty Bot on Farcaster as an example; users can post bounties externally, but this adds friction.
By interacting with users, AI Agents can provide convenience, utility, and ways to extract value in existing applications. But note: not all applications are created to support AI Agents; those with irreparable APIs, such as Farcaster, are best suited.
I wrote a paper on the main legal issues of agents on Web2 platforms. My research indicates that if users have complete control over the agents, and Web2 platforms try to block the agents, users would 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.
Secondly, helping humans work. Humans are good at signaling but have poor execution. Agents bridge this gap by doing the heavy lifting while humans guide the outcomes through preferences.
A great example is BottoDAO. The artworks it creates are influenced by the input of DAO token holders. Artificial intelligence does the hard work of creating art, but humans guide its creative direction through their preferences in voting on the artworks.
Thirdly, aggregating and organizing information. Agents can handle vast amounts of data, far beyond human capabilities. For example, trading bots analyze large amounts of on-chain data to make decisions.
Finally, entertainment value. This may be the most discussed 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. But 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 interesting way like KOLs.
The advantage of agents as KOLs is that once they have a fixed audience, they can easily provide other services, especially those that can bring more direct benefits to the agents than advertising.