Author: Daniel Barabander, Partner at Variant Fund; Translated by: 0xjs@Golden Finance
What are agents good at? We discussed this internally and summarized at least four points:
1. Close to human needs; 2. Assist in driving human actions; 3. Integration and synthesis of information; 4. Provide entertainment.
First, close to human needs. Agents can process human language, so theoretically, any application that a human can use as a user can also be utilized by agents. However, unlike human users, agents can provide services at scale to other users on these platforms.
Therefore, agents can act as a layer on top of existing applications that are beloved by users, expanding their practicality. Take the Bounty Bot on Farcaster as an example. Users could originally post bounties externally, but that would increase operational difficulty. Waiting for the Merkle team to develop this feature takes time. The Bounty Bot provides a service based on this.
By being close to where users are (meeting existing user scenarios), agents provide convenience and practicality, paving the way for value acquisition in existing applications. However, it's important to note that not all applications excel equally in supporting agents — the best applications to build on are those with reliable application programming interfaces (APIs), like Farcaster.
By the way, I self-recommend: I wrote a paper on a major legal concern regarding agents on Web2 platforms — the Computer Fraud and Abuse Act (CFAA). In short: my research indicates that if users have complete control over the credentials of an agent, and the Web2 platform bans that agent, then the user must stop running that agent. My paper emphasizes building agents on coordinated/open platforms like Farcaster, which is another reason I am particularly interested in agents on that platform.
Secondly, assisting in driving human actions. Humans excel at signaling (indicating intentions, etc.), but they face difficulties in execution (especially in a networked environment). Agents bridge this gap by taking on heavy workloads, while humans guide the outcomes through their preferences.
A great example of an agent utilizing this is BottoDAO. It creates art based on the opinions of decentralized autonomous organization (DAO) token holders. Artificial intelligence handles the difficult part of art creation, while human preferences, reflected by voting on the artworks, guide its creative direction.
Third, integration and synthesis of information. Agents can process vast amounts of data, far beyond human capability. For example, trading bots analyze large volumes of on-chain data to make decisions. Others, like the @aixbt_agent on Twitter, mine valuable information from crypto Twitter (CT).
Finally, providing entertainment. In the crypto space, this may be the category where agents develop the fastest, as seen with Truth Terminal.
Of course, the entertainment brought by agents on social platforms lies in the novelty of robot-generated content. But I am more interested in robots creating entertaining content based on their strengths, interacting with other users in interesting ways, just like any influencer on the platform.
The beauty of agents as influencers lies in the fact that, like traditional influencers, once they attract a loyal audience, they can easily offer other agent services, especially those that are easier to directly monetize for agents than off-chain advertising.
