AI Agents Becoming Economic Actors: What Infrastructure Gaps Can Blockchain Fill?

Agents Can Execute Tasks, but Struggle to Become Collaborative Economic Units

Advancements in large language models and toolchains now enable AI Agents to continuously call APIs, perform procurement, and orchestrate complex, multi-step tasks. Yet, as their capabilities grow, underlying system-level challenges become more pronounced: Agents across different platforms lack a unified way to prove “who they represent,” “what they’re authorized to do,” and “how they receive or make payments.” In sectors like financial services, non-human automated entities already vastly outnumber human roles. As general-purpose Agents are deployed in more industries, challenges such as non-transferable identities, non-programmable default payments, and collaboration isolated in data silos will only intensify.

In a recent article, a16z crypto described these gaps as the missing infrastructure preventing Agents from becoming true economic participants, highlighting five key pillars: identity, governance, payments, trust, and user control. The following sections break down these dimensions and, at the end of each, add engineering boundary considerations to distinguish between current narratives and established industry standards.

Identity and Approval: From “Unbanked” to Verifiable Entities

The article notes that many Agents are effectively “unbanked”: they can interact with financial or data services, but lack portable, verifiable, and third-party-recognized identity and approval mechanisms. While human systems depend on KYC and credit records, the industry is introducing concepts like KYA (Know Your Agent), which leverage cryptographic credentials to bind entities, permissions, constraints, and reputation.

Blockchain’s primary role here is as a neutral coordination layer: Wallets based on Public Key cryptography, on-chain or off-chain verifiable credentials, and registration data that can be parsed across applications allow an Agent to operate independently of any single platform’s account system, while still providing counterparties with the minimum trust evidence required. Current implementations include on-chain Agent registries, Agent forms integrated with Stablecoin Wallets, and exploration of relevant ERC proposals. It’s important to emphasize that universal identity standards have not yet converged; merchants and compliance teams may still block Agents lacking mutually recognized credentials at gateways. On-chain solutions address technical expression but do not replace regulatory classification or access requirements.

Governance and Execution: Can On-Chain Rules Restrain Off-Chain Model Power?

When Agents participate in resource allocation or process decisions, control becomes a core issue. If the underlying model and inference services are tightly controlled by a single provider, then even if upper layers appear decentralized through voting, operators can still alter outcomes by updating models, adjusting constraints, or overriding behaviors. The article argues that without assurances regarding training data, prompts, behavior logs, and post-deployment immutability, Agent governance can easily devolve into “whoever controls the weights, governs.”

Blockchain can contribute by:

• Writing validated collective decisions on-chain and executing them automatically, narrowing the gap between “decision” and “execution”;
• Retaining transparent logs of Agent behavior (with careful design of on-chain scope to balance privacy and auditability);
• Combining with user-migratable Agent configurations to reduce the risk of unilateral rule changes via silent updates from a single application.

However, limitations are clear: on-chain records cannot independently prove off-chain models haven’t been manipulated; cryptography and smart contracts can strengthen rule and fund flow constraints, but cannot substitute for independent audits of model supply chains or runtime environments.

Payments and Settlement: Machine-to-Machine Commerce and Stablecoin Settlement Layers

The market for Agent-driven services is expanding: services expose prices via schemas and endpoints, allowing callers to complete authentication, payment, and data retrieval in a single request—eliminating the need for traditional checkout pages. Traditional payment networks face high underwriting costs for “headless merchants” lacking websites or legal entities. Stablecoins on open networks enable programmable settlement, letting Developers embed payment logic directly at the endpoint layer, without requiring traditional acquirer integration.

Industry solutions are embedding payments into HTTP request flows (such as x402), and integrating with aggregator marketplaces and cloud providers. The article notes that, after filtering out wash trading and similar anomalies, transaction volumes remain at an early stage, though toolchain and platform integration is progressing rapidly. For decision-makers, the key consideration is whether, as Agents become default purchasers, demand persists for programmable, frictionless, machine-readable settlement layers. If so, public chains, Stablecoins, and card-based solutions will compete primarily on underwriting models, compliance pathways, and Developer experience—not just on technology labels.

Trust Mechanisms: Architectural Choices as Verification Costs Rise

As the marginal cost of intelligence and execution falls, the relative cost of verification and accountability rises; if Agent throughput far exceeds human supervision capacity, “human-in-the-loop” solutions face hard scalability limits. Unverified automation can accumulate compounding risks: systems may optimize superficial metrics while diverging from human intent, with risks only surfacing later.

In this context, blockchain technologies serve to move part of the trust boundary into the system architecture itself: verifiable origins, on-chain proofs, and accountability trails tied to fund flows allow organizations to clarify “who did what and how responsibility is traced when issues arise.” This doesn’t mean all content must be on-chain; in practice, it’s more common to selectively anchor key commitments and states, balancing cost against auditability.

User Control: Delegation Boundaries and Executable Permissions

As users shift from step-by-step operations to “describing outcomes and having the system execute them autonomously,” authorization granularity increases and failure modes become less visible: ambiguous inputs, unreported interruptions, and single consents triggering extended action chains. The article highlights crypto-native tools such as scoped delegation (restricting Agent-accessible resources and amounts at the contract or policy level) and intent-based interactions (users declare target states, with solvers finding compliant paths), all aimed at reducing blind trust and increasing predictability of Agent behavior.

When evaluating these solutions, it’s vital to distinguish between product usability and the strength of security guarantees: UI-level delegation and on-chain executable constraints don’t always align. If critical restrictions exist only at the application layer and aren’t bound to settlement or identity credentials, they may still be bypassed or misconfigured.

Summary: Reasonable Expectations from an Infrastructure Perspective

In line with a16z crypto’s analysis and the current state of the industry, a prudent view is that blockchain and related cryptographic infrastructure are well positioned to serve as identity carriers, settlement layers, rule execution engines, and audit anchors within the Agent economy, helping address standardization gaps in cross-platform collaboration and machine-to-machine commerce. At the same time, aligning model behavior with on-chain declarations, mutual recognition of identity standards, and regulatory frameworks will require ongoing efforts across communities and institutions. Viewing blockchain as “exoskeletal” infrastructure—reinforcing coordination, settlement, and verifiable boundaries, rather than replacing model intelligence itself—offers a more objective technical perspective.

Actionable next steps include: mapping identity sources, payment pathways, and log retention requirements within your own Agent call chains; piloting on-chain anchoring and Stablecoin settlement compliance in test scenarios; and distinguishing “auditable design” from marketing claims in vendor solutions. These steps aren’t inherently tied to public chain adoption, but are directly relevant to risk management as Agent deployment scales.