a16z: 5 Ways Blockchain Supports AI Agent Infrastructure

Author: a16z

Translation: Hu Tao, ChainCatcher

Artificial intelligence agents are rapidly shifting from “co-pilots” to economic participants, at a pace that even surpasses the surrounding infrastructure.

Although agents can now perform tasks and execute trades, they lack standardized methods to prove their identity, authority, and compensation across environments. Identity information cannot be shared across platforms, payment methods are not yet programmable by default, and coordination work is conducted independently.

Blockchain solves this problem at the infrastructure layer. Public ledgers provide receipts for every transaction, allowing anyone to audit. Wallets offer portable identity information for users. Stablecoins provide an alternative settlement method. These are not distant future technologies. They are available now and can help users operate as true economic entities without permission.

1. Non-human identities

The current bottleneck in the agent economy 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 exceeds human employees by about 100 times. As large-scale deployment of modern agent frameworks (using tools like LLMs, autonomous workflows, multi-agent orchestration) becomes widespread, this ratio will continue to rise across all sectors.

However, these agents still do not have bank accounts in practice. They can interact with financial systems, but the interaction methods lack portability, verifiability, and are not inherently trusted. They lack standardized proof of permissions, cannot operate independently across platforms, and cannot be held accountable for their actions.

What is missing is a universal identity layer—akin to an SSL protocol for agents—that standardizes coordination across platforms. While there are significant efforts, the approaches remain fragmented: one side is a vertically integrated, fiat-first stack; the other side involves crypto-native, open standards (such as x402 and emerging agent identity proposals); and developer frameworks like MCP (Model Context Protocol) extend to bridge identities 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 authorized to do, and how it gets paid. This is the core idea behind KYA (Know Your Agent).

Just as humans rely on credit history and KYC (Know Your Customer), agents need cryptographic signatures and credentials that bind the agent to its delegator, permissions, constraints, and reputation. Blockchain provides a neutral coordination layer for all this: portable identities, programmable wallets, and verifiable proofs that can be parsed in chat apps, APIs, and marketplaces.

Early implementations are emerging: on-chain agent registries, native wallets using USDC, ERC standards for “trust-minimized agents,” and developer toolkits combining identity with embedded payments and fraud controls.

But until a universal identity standard is established, merchants will still block agents at firewalls.

2. Governance of AI operating systems

Agents are beginning to operate real systems, raising new questions.

The key issue is who truly controls everything. Imagine a community or company where AI systems coordinate critical resources—whether allocating funds or managing supply chains. Even if policies are decided by voting, if the underlying AI layer is controlled by a single vendor, which can push model updates, adjust constraints, or overturn decisions, that power is very fragile. Formal governance layers may be decentralized, but the operational layer remains centralized; whoever controls the models ultimately controls the outcomes.

When agents assume governance roles, they introduce a new dependency layer. In theory, this could make direct democracy easier: everyone could have an AI representative responsible for understanding complex proposals, weighing pros and cons, and voting according to their declared preferences.

But this vision only works if these agents are truly accountable to the people they represent, can operate across different service providers, and are technically limited to following human instructions. Otherwise, the system may appear democratic on the surface but is actually driven by opaque model behaviors, which no one can control.

If current reality is that agents are built from a small number of foundational models, we need ways to prove that their behaviors align with user interests, not just model company profits. This may require multi-layered cryptographic guarantees: (1) what training data, fine-tuning, or reinforcement learning processes the models originate from; (2) the exact prompts and instructions controlling specific agents; (3) logs of their real-world actions; and (4) reliable assurances that providers cannot alter instructions or retrain models to operate covertly. Without these guarantees, governance of agents devolves into control by those who hold the model weights.

This is where cryptocurrency plays a role. If collective decisions are recorded on-chain and automatically enforced, AI systems can be required to execute verified outcomes. If agents have cryptographic identities and transparent execution logs, we can verify whether they follow rules. Moreover, if the AI layer is user-owned and portable, not locked into a single platform, no company can unilaterally change rules via model updates.

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

3. Filling the gap in traditional payment systems for AI-native enterprises

AI agents are starting to purchase—web scraping, browsing sessions, image generation—and stablecoins are becoming the alternative settlement layer for these transactions. Meanwhile, a new class of agent-facing marketplaces is emerging. For example, Stripe and Tempo’s MPP marketplace aggregates over 60 services designed specifically for AI agents. In its first week, it processed over 34,000 transactions, with fees as low as $0.003, and stablecoins are among the default payment options.

What differs is how these services are accessed. No checkout pages. Agents read schemas, send requests, pay, and receive outputs in a single exchange. They represent a new kind of “headless” merchant: just a server, a set of endpoints, and a price per call. No front end—no storefront or sales team.

Payment rails enabling this are already live. Coinbase’s x402 and MPP adopt different approaches but embed payments directly into HTTP requests. Visa is also expanding similar card rails, providing CLI tools that let developers spend from the terminal, with merchants receiving stablecoins instantly in the backend.

Data remains early-stage. After filtering out wash trading and non-organic activity, x402 handles roughly $1.6 million per month in agent-driven payments, far below Bloomberg’s recent report of $24 million (based on x402.org data). But the surrounding infrastructure is rapidly expanding: Stripe, Cloudflare, Vercel, and Google have integrated x402 into their platforms.

The developer tools space holds enormous potential. The rise of Vibe Coding broadens the community of software developers and expands the potential market for developer tools. Companies like Merit Systems are building future-oriented solutions, launching AgentCash—a CLI wallet and marketplace platform connecting to MPP and x402 protocols. These products enable agents to use stablecoins from a single account to buy data, tools, and functionalities. For example, sales agents can call a single endpoint to access data from Apollo, Google Maps, and Whitepages to enrich leads, all from the command line.

The reason these agent-to-agent business models favor crypto payments (and emerging card-based solutions) is severalfold. First, underwriting: when a payment processor integrates with a merchant, it assumes that merchant’s risk. A headless merchant without a website or legal entity is hard to underwrite with traditional processors. Second, stablecoins are programmable on open networks: any developer can enable endpoints to support payments without integrating payment processors or signing merchant agreements.

We’ve seen this pattern before. Every shift in business models creates a new wave of merchants, and existing systems initially struggle to serve them. Companies building this infrastructure are betting not on $1.6 million monthly revenue but on what the revenue could be once agents become the default buyers.

4. Repricing trust in the agent economy

For three hundred thousand years, human cognition has been the bottleneck limiting progress. Now, AI is pushing the marginal cost of execution toward zero. As scarcity resources become abundant, constraints shift. When intelligence becomes cheap, what becomes expensive? Verification.

In the agent economy, the real limit to scale is our biological instinct to audit and evaluate machine decisions. The throughput of AI has far surpassed human oversight capacity. Because supervision is costly and failures take time to surface, markets tend to reduce oversight investments. “Human-AI collaboration” is rapidly becoming an impossible ideal.

Deploying unverified agents introduces compounded risks. Systems relentlessly optimize “agent” metrics while subtly diverging from human intent, creating a false productivity illusion and hiding massive AI debt accumulation. To safely entrust the economy to machines, trust can no longer rely solely on manual audits—trust must be embedded into the architecture.

When anyone can generate content for free, the most critical factor is verifiable provenance—knowing where content comes from and whether it’s trustworthy. Blockchain, on-chain attestations, and decentralized digital identity systems are reshaping the economic boundaries of secure deployment. AI is no longer a black box but has a clear, auditable history.

As more AI agents begin to transact with each other, settlement mechanisms and provenance systems become intertwined. Payment systems—like stablecoins and smart contracts—can carry cryptographic receipts, recording who did what and who is responsible if issues arise.

Humans’ comparative advantage continues to grow: from catching small errors to devising strategic directions and taking responsibility when problems occur. Long-term advantage belongs to those who can cryptographically certify outputs, insure them, and assume responsibility for failures.

Unverified scale is a risk that accumulates over time.

5. Preserving user control

Decades of layered abstractions have continually reshaped how users interact with technology. Programming languages abstracted machine code. GUIs replaced command lines, then mobile apps and APIs took over. Each wave concealed more underlying complexity while keeping users in control of the overall system.

In the world of agents, users specify desired outcomes rather than actions, and the system determines how to achieve them. Agents not only abstract task completion but also the executor of tasks. After setting initial parameters, users step back, and the system runs autonomously. The user’s role shifts from interaction to supervision; unless intervened, the system remains “on” by default.

As users delegate more tasks to agents, new risks emerge: ambiguous inputs may cause agents to act on incorrect assumptions without user awareness; failures may go unreported, leaving no clear diagnostic path; a single approval could trigger multi-step workflows that no one anticipated.

Encryption technology plays a crucial role here. The core of cryptography is to minimize blind trust. As users entrust more decision-making to software, agent systems make this issue more prominent and demand more rigorous system design—defining clearer boundaries, increasing transparency, and providing stronger guarantees about system functions.

To meet this challenge, next-generation cryptographic tools are emerging. For example, MetaMask’s Delegation Toolkit, Coinbase’s AgentKit and agent wallets, and Merit Systems’ AgentCash—scope-based delegation frameworks—allow users to define what agents can and cannot do at the smart contract level. Intent-based architectures like NEAR Intents (which, since Q4 2024, have seen over $15 billion in decentralized exchange volume on (DEX)) enable users to specify desired outcomes—such as “bridge tokens and stake”—without detailing the implementation.

AI makes scaling inexpensive but trust difficult to establish. Cryptocurrency can fundamentally rebuild trust at scale.

Internet infrastructure is under construction, enabling individuals to directly participate in economic activity. The question now is whether it will be designed for maximum transparency, accountability, and user control, or built on systems inherently unsuitable for non-human actors.