Original source: a16z crypto

Original translation: AididiaoJP, Foresight News

AI Agents are rapidly evolving from auxiliary tools to real economic participants at a speed far exceeding other infrastructures.

Although Agents can now perform tasks and transactions, they still lack standard ways to prove "who I am," "what I am authorized to do," and "how I can be compensated" across environments. Identity cannot be migrated, payments are not yet programmably default, and collaboration remains in isolated states.

Blockchain is addressing these issues from the infrastructure level. Public ledgers provide audit trails for every transaction that anyone can verify; wallets give Agents portable identities; stablecoins serve as another settlement layer. These are not concepts for the future; they are already available today, enabling Agents to operate as real economic entities in a permissionless manner.

Providing Identity for Non-Humans

The current bottleneck in the Agent economy is no longer intelligence, but identity.

In the financial services sector alone, the number of non-human identities (automated trading systems, risk engines, fraud models) is around 100 times that of human employees. With the large-scale deployment of modern Agent frameworks (tool-calling large models, autonomous workflows, multi-Agent orchestration), this ratio will continue to rise across industries.

However, these Agents are still operating in a "unbanked" state. They can interact with the financial system but cannot do so in a portable, verifiable, and default trustworthy manner. They lack standardized ways to prove their authority, operate independently across platforms, or take responsibility for their actions.

What is missing is a universal identity layer—equivalent to an Agent version of SSL—capable of standardizing collaboration across platforms. Current solutions remain fragmented: on one hand, there is a vertically integrated, fiat-prioritized stack; on the other, there are crypto-native, open standards (like x402 and emerging Agent identity proposals); and there are developer framework extensions trying to bridge application layer identities (like MCP, Model Context Protocol).

There is still no widely adopted, interoperable way for one Agent to prove to another Agent: who it represents, what it is allowed to do, and how it gets compensated.

This is the core idea of KYA (Know Your Agent). Just as humans rely on credit histories and KYC (Know Your Customer), Agents will need cryptographically signed certificates that bind them to their subjects, authorities, constraints, and reputations.

Blockchain provides a neutral coordination layer: portable identities, programmable wallets, and verifiable proofs that can be parsed in chat applications, APIs, and marketplaces.

We have already seen early implementations emerge: on-chain Agent registries, wallet-native Agents using USDC, ERC standards for "minimally trusted Agents," and developer toolkits that integrate identity with embedded payments and fraud controls.

But until a universal identity standard emerges, merchants will continue to block Agents at the firewall.

Governing AI Operational Systems

As Agents begin to take over real systems, a new question arises: who truly holds the control? Imagine a community or company, where an AI system coordinates critical resources (whether allocating capital or managing supply chains).

Even if people can vote on policy changes, if the underlying AI layer is controlled by a single provider capable of pushing model updates, adjusting constraints, or overriding decisions, then that authority is very fragile. The formal governance layer may be decentralized, but the operational layer remains centralized—whoever controls the model ultimately controls the outcomes.

When Agents assume governance roles, they introduce a new dependency layer. In theory, this can make direct democracy more feasible: everyone can possess an AI agent to help understand complex proposals, model trade-offs, and vote according to established preferences.

However, this vision can only be realized if Agents are truly accountable to those they represent, can be ported across providers, and are technically constrained to follow human directives. Otherwise, the systems appear democratic on the surface, but are effectively manipulated by opaque model behaviors controlled by no one.

If the current reality is that Agents are primarily built on a few foundational models, we need ways to prove that an Agent acts in the user's interest and not in the interests of the model company.

This will likely require offering cryptographic assurances at multiple levels:

(1) Training data, fine-tuning, or reinforcement learning upon which the model instance is based;

(2) The exact prompts and instructions followed by a specific Agent;

(3) Its actual behavioral records in the real world;

(4) Trusted guarantees that the provider cannot change its instructions or retrain it without the user's knowledge after deployment. Without these assurances, Agent governance will degrade into governance by those controlling the model weights.

This is where cryptographic technology can particularly shine. If collective decisions are recorded on the blockchain and automatically executed, AI systems can be required to strictly adhere to verified outcomes. If Agents possess cryptographic identities and transparent execution logs, people can check whether their agents are operating within boundaries.

If the AI layer is user-owned and portable, rather than locked into a single platform, no company can change the rules with a single model update.

Ultimately, governing AI systems is fundamentally an infrastructure challenge, not a policy challenge. True authority depends on building enforceable guarantees within the system itself.

Bridging the Gap of Traditional Payment Systems for AI-Native Businesses

AI Agents are starting to purchase various services—web scraping, browser sessions, image generation—stablecoins are becoming an alternative settlement layer for these transactions. Meanwhile, a new market for Agents is emerging.

For example, the MPP market from Stripe and Tempo aggregates over 60 services specifically designed for AI Agents. In its first week, it processed over 34,000 transactions, with fees as low as $0.003, and stablecoins as one of the default payment methods.

The difference lies in the access method for these services: there are no checkout pages. Agents read schemas, send requests, pay, and receive outputs, all within a single exchange.

This represents a new class of identity-less merchants: just one server, a set of endpoints, and a price per call. No front-end interface, no sales team.

The payment rails to achieve this are already in place. Coinbase's x402 and MPP take different approaches, but both embed payments directly into HTTP requests. Visa is also extending card payment rails in a similar direction, offering a CLI tool that allows developers to spend from the terminal, with merchants instantly receiving stablecoins in the back end.

Data is still in early stages. After filtering out non-organic activities like spam, x402 processes around $1.6 million in Agent-driven payments monthly, far less than Bloomberg's recent report of $24 million (citing x402.org data). However, peripheral infrastructure is rapidly expanding: Stripe, Cloudflare, Vercel, and Google have all integrated x402 into their platforms.

Developer tools represent a significant opportunity, as "vibe coding" expands the population of people able to build software, the total addressable market for developer tools is also growing. Companies like Merit Systems are building products for this world, such as AgentCash—a CLI wallet and marketplace connecting MPP and x402. These products allow Agents to purchase required data, tools, and capabilities using stablecoins from a single balance.

For example, a sales team’s Agent can call an endpoint while fetching data from Apollo, Google Maps, and Whitepages to enrich lead information, all without the user leaving the command line.

This Agent-to-Agent commerce is inclined to use cryptographic payment rails (along with emerging card-based solutions) for several reasons.

First is underwriting risk: Traditional payment processors need to take on merchant risk when onboarding, and a headless merchant without a website or legal entity is hard to underwrite by traditional processors.

Second is the permissionless programmability of stablecoins on open networks: Any developer can enable a payment supporting endpoint without needing to onboard a payment processor or sign a merchant agreement.

We have seen this model before. Every transformation in business forms creates a new type of merchant that existing systems initially struggle to serve. Companies building this infrastructure are betting not on $1.6 million per month, but on what that number will become when Agents become the default buyers.

Repricing Trust in the Agent Economy

For the past 300,000 years, human cognition has been the bottleneck of progress. Today, AI is pushing the marginal cost of execution toward zero. As scarce resources become abundant, constraints shift. When intelligence becomes cheap, what becomes expensive? The answer is validation.

In the Agent economy, the real limit to scalability is our biologically constrained ability to audit and underwrite machine decisions. The throughput of Agents has far exceeded human oversight capabilities. Due to the high costs of supervision and the lagging nature of failures, markets tend to underinvest in oversight. "Humans in the loop" is rapidly becoming physically impossible.

However, deploying unverified Agents introduces compound risks. Systems will ruthlessly optimize for "agent" metrics while quietly deviating from human intentions, creating hollow appearances of productivity that obscure the accumulation of immense AI debts. To safely delegate the economy to machines, trust can no longer rely on manual checks—trust must be hardcoded into the system architecture itself.

When anyone can generate content for free, the most important factor is verifiable sources—knowing where it comes from and whether you can trust it. Blockchain, on-chain proofs, and decentralized digital identity systems are changing the economic boundaries of what can be safely deployed. You no longer treat AI as a black box, but instead gain clear, auditable records of history.

As more AI Agents begin to trade with each other, settlement rails and provenance proofs start to closely intertwine.

Systems handling funds (like stablecoins and smart contracts) can also carry cryptographic proofs that show who did what, and who should be held accountable if something goes wrong.

The comparative advantage of humans will migrate upward: from finding small errors to setting strategic directions and taking responsibility when things go wrong. Lasting advantages belong to those who can certify outputs cryptographically, provide insurance for them, and absorb responsibility in case of failure.

Unvalidated scalability is a liability that will accumulate over time.

Maintaining User Control

For decades, new layers of abstraction have defined how users interact with technology. Programming languages abstracted away machine code; command lines gave way to graphical user interfaces, followed by mobile apps and APIs. Each transition hides more underlying complexity while always keeping users firmly in the loop.

In the world of Agents, users specify outcomes rather than specific actions, and the system decides how to achieve them. Agents not only abstract the way tasks are executed but also who executes them. Users set initial parameters, then take a step back and let the system run itself. The user’s role shifts from interaction to oversight; the default state is "on," unless the user intervenes.

As users delegate more tasks to Agents, new risks arise: ambiguous inputs could lead Agents to act based on erroneous assumptions without the user’s knowledge; failures might go unreported, making clear diagnostics impossible; a single approval could trigger unforeseen multi-step workflows.

This is where cryptographic technology can help. Cryptographic methods have always aimed to minimize blind trust.

As users delegate more decisions to software, Agent systems sharpen this issue and elevate our rigor in design by requiring clearer limits, improved visibility, and stronger guarantees regarding system capabilities.

A new generation of crypto-native tools is emerging. Scope delegation frameworks—for example, MetaMask's Delegation Toolkit, Coinbase's AgentKit and Agent Wallet, and Merit Systems’ AgentCash—allow users to define what Agents can and cannot do at the smart contract level. Intent-based architectures (such as NEAR Intents, which have processed over $15 billion in cumulative DEX transaction volume since Q4 2024) enable users to set expected outcomes (such as "bridge tokens and stake") without needing to specify how to achieve them.

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