AI agents are rapidly transitioning from “co-pilots” to economic actors, outpacing even the surrounding infrastructure. While agents can now perform tasks and execute transactions, they lack standardized methods for proving their identity, permissions, and how they get paid across environments. Identity information cannot be shared across platforms, payment methods are not yet programmatically available by default, and coordination efforts are carried out independently. Blockchain solves this problem at the infrastructure layer. Public ledgers provide receipts for every transaction, which anyone can audit. Wallets provide users with portable identity information. Stablecoins offer an alternative settlement method. These are not distant future technologies. They are available now and can help users operate like true economic entities without permission.

1. The Non-Human Identity Agent Economy
   The current bottleneck is no longer intelligence, but identity. In the financial services industry alone, the number of non-human identities (automated trading systems, risk engines, fraud models) already outnumbers human employees by approximately 100x. With the large-scale deployment of modern agent frameworks (LLMs using tools, autonomous workflows, multi-agent orchestration), this ratio is bound to continue rising across all industries. However, these agents still don’t really have bank accounts. They can interact with financial systems, but in ways that lack portability, verifiability, and are not trusted by default. They lack a standardized way to prove permissions, cannot operate independently across platforms, and cannot be held accountable for their actions.

What is currently missing is a universal identity layer—the equivalent of an SSL protocol for agents, which standardizes coordination across platforms. While there are significant attempts, the methods are still fragmented: on one side are vertically integrated, fiat-first stacks; on the other side are crypto-native, open standards (such as x402 and emerging agent identity proposals); and there are extensions of developer frameworks such as MCP (Model Context Protocol) that attempt to bridge identity at the application layer. There is still no widely adopted, interoperable way for one agent to prove to another: who it represents, what it is allowed to do, and how it gets paid. This is the core idea of KYA (Know Your Agent).

1. Governance of AI Operating Systems
   As agents begin to operate real systems, this raises some new questions. The key is who is really in control. Imagine a community or company where an AI system is responsible for coordinating critical resources, whether it’s allocating funds or managing the supply chain. Even if people vote to change policies, this power is very weak if the underlying AI layer is controlled by a single vendor who can push model updates, adjust constraints, or overturn decisions. The formal governance layer may be decentralized, but the operating layer remains centralized; whoever controls the model ultimately controls the outcome.

When agents take on governance roles, they introduce a new layer of dependency. In theory, this could make direct democracy easier to implement: everyone could have an AI representative responsible for understanding complex proposals, weighing the pros and cons, and voting according to their stated preferences. But this vision can only be realized if these agents are truly accountable to the people they represent, are universal across service providers, and are technically limited to following human instructions. If the current reality is that agents are built from a small number of foundation models, then we need ways to prove that the agent’s behavior is in the user’s interest, not the model company’s.

1. Filling the Gaps in Traditional Payment Systems for AI-Native Businesses
   AI agents are starting to buy things—web scraping, browser sessions, image generation—and stablecoins are becoming an alternative settlement layer for these transactions. At the same time, a new class of agent-oriented marketplaces is taking shape. For example, Stripe and Tempo’s MPP marketplace, which aggregates more than 60 services designed specifically for AI agents. In its first week online, it processed over 34,000 transactions with fees as low as $0.0030, with stablecoins as one of the default payment methods. The difference is the way these services are accessed. There is no checkout page. The agent reads the schema, sends the request, pays, and receives the output in one exchange.

The data is still in the early stages. After filtering out non-organic activity such as wash trading, x402 processes approximately $1.60M in agent-driven payments per month, far less than the $24.00M recently reported by Bloomberg (citing x402.org data). But the surrounding infrastructure is expanding rapidly: Stripe, Cloudflare, Vercel, and Google have all integrated x402 into their platforms. This agent-to-agent commerce tends to favor crypto payments because of underwriting risk and the permissionless programmability of stablecoins on open networks.

1. Repricing Trust in the Agent Economy
   For three hundred thousand years, human cognition has been the bottleneck constraining progress. Today, AI is pushing the marginal cost of execution toward zero. When scarce resources become abundant, the constraints also shift. When intelligence becomes cheap, what becomes expensive? Verification. In the agent economy, the real limit to scaling is the limitations of our biological instincts, our ability to audit and evaluate machine decisions. Agent throughput already far exceeds human oversight capacity.

To safely entrust the economy to machines, trust can no longer rely on manual review—trust must be hard-coded into the architecture itself. When anyone can generate content for free, the most important thing is verifiable provenance—knowing where the content came from and whether it is trustworthy. Blockchain, along with on-chain authentication and decentralized digital identity systems, changes the economic boundaries of secure deployment. AI is no longer seen as a black box, but as having a clear, auditable history.

1. Retaining User Control
   For decades, layers of abstraction have been changing the way users interact with technology. In the agent world, users specify outcomes rather than actions, and the system decides how to achieve those outcomes. Agents abstract not only how tasks are accomplished, but also who performs them. After the user sets the initial parameters, they step back and the system runs on its own. As users delegate more tasks to agents, new risks arise: vague inputs can cause agents to take actions without the user’s knowledge; failures may not be reported; and a single approval can trigger an unexpected multi-step workflow.

This is where crypto comes in. At its core, crypto has always been about minimizing blind trust. To address this challenge, a new generation of crypto-native tools is emerging. For example, scope-based delegation frameworks such as MetaMask’s Delegation Toolkit, Coinbase’s AgentKit and agent wallets, and Merit Systems’ AgentCash allow users to define at the smart contract level what actions an agent can and cannot perform.

AI makes scaling inexpensive, but it makes building trust difficult. Cryptocurrency can rebuild trust at scale. The internet infrastructure is being built in which individuals can participate directly in economic activity. The question now is whether it will be designed with maximum transparency, accountability, and user control in mind, or whether it will be built on systems that are not well-suited for non-human actors.

[a16z]