Smart agents in the market

Author: Daniel Barabander, Partner at Variant Fund; Translated by: AIMan@Jinse Finance

If the future of the internet is a market where agents (also known as intelligent agents) pay each other to provide services, then cryptocurrencies will find a mainstream product market fit that they could only dream of before. While I believe that agents will pay each other to provide services, I am not quite sure if this market model will be successful.

What I mean by "market" refers to a decentralized, permissionless ecosystem composed of independently developed and loosely coordinated agents — the internet is more like an open marketplace rather than a centralized planned system. Linux is a typical example of the "market" model. This contrasts with the "cathedral" model: services that are strictly controlled and vertically integrated, managed by a few large participants. Windows is a typical example. (The term originates from Eric Raymond's classic essay "The Cathedral and the Bazaar," which describes open source development as a chaotic yet adaptive system — an evolving system that can surpass meticulously planned structures over time.)

Let's analyze each condition - the rise of proxy payments and marketplaces - and then explain why, if both are realized, cryptocurrency not only becomes useful but also necessary.

Two Conditions

Condition 1: Payments will be integrated into most agency transactions.

The internet as we know it subsidizes costs by selling ads based on the number of page views. However, in a world composed of agents, people will no longer need to visit websites to access online services. Applications will also increasingly be based on agents rather than user interfaces.

Agencies lack the eyeballs to attract advertisers, therefore, there is a strong reason for applications to adjust their profit strategies and charge service fees directly to the agencies. This is basically the current state of the API—services like LinkedIn are free, but if you want to use the API (that is, the "bot" users), you have to pay.

In light of this, payment functions are likely to be integrated into most proxy transactions. Agents will provide services and charge users/agents fees in a microtransaction manner. For example, you can have your personal agent search for suitable job candidates on LinkedIn ###LinkedIn(. The personal agent will communicate with the LinkedIn recruitment agent, who will charge service fees in advance.

Condition 2: Users will rely on agents built by independent developers that have ultra-specialized prompts/data/tools, forming an untrusted agent marketplace of mutually calling services.

This situation makes sense in principle, but I'm not sure how it will play out in practice.

The following are the reasons for the formation of the market:

• Currently, humans undertake the vast majority of service work, and we use the internet to solve some discrete tasks. However, with the rise of intelligent agents, the range of tasks we delegate to technology will expand dramatically. Users will need specialized prompts, tool calls, and data from intelligent agents to perform their specific tasks. These tasks are diverse, and a small group of trusted companies will find it difficult to cover them, much like how the iPhone relies on a large third-party developer ecosystem to fully realize its potential.
• Independent agent developers will fill this role, as they can create professional agents through very low development costs (e.g., ambient coding) and open-source models. This will form a long-tail market composed of agents that provide highly accurate data/prompts/tools, creating a "bazaar". Users will request agents to perform tasks, and these agents will call upon other specialized agents to complete the tasks, which in turn will call upon other agents, forming a long daisy chain (Note: a daisy chain is a type of network topology in which devices are connected to a single chain or loop).

In this market scenario, the vast majority of service-providing agents are relatively untrusted because they are provided by unknown developers and have niche applications. Agents in the long tail find it difficult to establish sufficient reputation to gain trust. This trust issue is particularly severe under the daisy chain paradigm, as trust from users tends to gradually weaken along each link in the daisy chain as services are delegated to agents that users trust (or can reasonably identify).

However, there are still many unresolved issues when considering how to implement this in practice:

• Let’s start with professional data as the main use case for agents in the market and look at an example to lay the foundation. Imagine a small law firm handling a large volume of transactions for cryptocurrency clients. The firm has hundreds of lists of negotiated terms. If you are a cryptocurrency company going through a seed round of financing, you can imagine that an agent will use a model fine-tuned according to these lists of terms to determine whether your terms align with market demand, which would be very useful.
• But if you think about it, is it really in the interest of law firms to make inferences about this data through representation? Making this service available to the public in the form of an API is essentially commoditizing the law firm's data, and what the law firm really wants is to increase the cost of lawyers' time. What about legal/regulatory considerations? The most valuable data often has legal systems that require it to be kept strictly confidential – which largely explains its value and why ChatGPT can't access it. But law firms are severely restricted from sharing this data due to confidentiality obligations. Even if the underlying data isn't shared directly, I'm very skeptical that the "fog" of neural networks is enough to reassure law firms that information won't be compromised. With all of this in mind, shouldn't law firms use this model internally, providing better legal services than their competitors while continuing to sell attorneys' time?
• In my opinion, the "sweet spot" between professional data and agency is that high-value data generated by non-sensitive businesses (e.g., healthcare, legal, etc.) can be used as an adjunct to their fee-based core services. For example, a shipping company (a non-sensitive business) generates a lot of valuable data in its shipping business (this is just my guess; I don't know anything about the shipping industry). As a result, the shipping company may be happy to hire an agency to utilize the data and charge a fee because the data is wasted in the first place. This data can be very valuable to some people (like hedge funds). But how many such scenarios are there? (This is not a rhetorical question; If you know of some good scenarios, please leave me a message. ）
• Regarding prompts and tool calls, I'm just not sure what kind of things independent developers will offer that are not mainstream enough to be productized by trusted brands. My simple thought is that if a prompt/tool call is valuable enough for independent developers to profit from, wouldn't a trusted brand step in and build a business on that? I think this is just my lack of imagination—there are plenty of ecological niche code repositories on GitHub that well parallel the agency situation. I welcome everyone to suggest some excellent use case examples.

If the actual situation does not support the market scenario, the vast majority of service-providing agents will be relatively trustworthy, as they will be developed by major brands. Agents can limit their interactions to a select group of trusted agents and rely on a trust chain to enforce service guarantees.

Why Cryptocurrency

If the internet becomes a marketplace composed of professional but essentially untrusted intermediaries (Condition 2) providing payment services (Condition 1), then the role of cryptocurrency will become clearer: it provides the guarantees necessary to underwrite transactions in a low-trust environment.

Although users will use online services recklessly when they are free (because the worst-case scenario is just wasting time), when it comes to money, users need to ensure that they receive the services they paid for. Nowadays, users obtain this guarantee through a "trust but verify" process. You trust the counterparty or platform you pay for the service and verify afterward that you received the service.

However, in a proxy marketplace, trust and post-verification are almost impossible to achieve.

• Trust. As mentioned above, agents in the long tail find it difficult to establish enough reputation to gain the trust of other agents.
• Post-verification. Agents will contact other agents in a long-chain manner, making it more challenging for users to manually verify and identify which agent has made a mistake or acted improperly.

The final result is that the "trust but verify" model we currently rely on will not be sustainable in this universe. And this is precisely where the advantage of cryptocurrency lies - exchanging value in an untrustworthy environment. Cryptocurrency achieves this by replacing trust, reputation, and post-hoc manual verification with cryptography and cryptoeconomics.

• Encryption: Service providers can only receive compensation when they can prove to the requesting service provider that they have indeed completed the promised tasks in an encrypted manner. For example, a provider can offer TEE certification or zkTLS proof (provided that the cost is low enough / the speed is fast enough) to demonstrate that they have scraped data from a certain website, run a specific model, or contributed a certain amount of computational power. These are all deterministic tasks that can be relatively easily verified through encryption.
• Cryptoeconomics: Agents providing services will stake an asset and be slashed if caught cheating, creating an economic incentive to act honestly even without trust. For example, an agent can research a topic and provide a report – but how do we know if it's "done well"? This is a more difficult form to verify because it is not deterministic, and achieving the right fuzzy verifiability has long been the holy grail of crypto projects. But I hope that we will eventually be able to achieve fuzzy verifiability by using AI as a neutral arbiter. We can imagine a dispute resolution/forfeiture process run by an AI committee in a trust-minimized environment, such as the Trusted Enforcement Environment )TEE###). When an agent objects to another agent's work, each AI in the committee has access to the inputs, outputs, and details about that agent's work (history of past disputes/network work, etc.). They can then decide whether or not to slash it. This will take on an optimistic verifiable form, where financial incentives will discourage all parties from cheating in the first place.

In fact, cryptocurrency enables us to atomize payments through proof of service - no agent will be compensated unless the work has been verifiably completed. In a permissionless agent economy, this is the only scalable way to ensure reliability at the margins.

In summary, if the vast majority of proxy trades do not involve payments (i.e., do not meet condition 1) or are conducted with trusted brands (i.e., do not meet condition 2), we may not need to set up encrypted channels for proxies. This is because, when money is not involved, users can safely interact with untrusted third parties; whereas when money is involved, proxies only need to whitelist a limited number of trusted brands/institutions to interact, and the trust chain can ensure that the promises of the services provided by each proxy are fulfilled.

But if both conditions are met, cryptocurrency will become an indispensable infrastructure, the only scalable way to validate work and execute payments in low-trust, permissionless environments. Cryptocurrency provides the tools that surpass cathedrals for marketplaces.