AI agents need on-chain identity before they control the digital economy
While you’re reading this, a silent workforce of AI agents is already at work: drafting and signing contracts, triggering payments, reallocating capital, optimizing supply chains, and querying sensitive corporate data. What began as simple recommendation engines is quickly evolving into a class of autonomous economic actors. Yet one foundational element is still missing: a robust, standardized way to prove who these agents are, what they are allowed to do, and who is responsible when they break the rules.
As these systems shift from “assistants” to “agents” with real financial authority, identity and authorization can no longer be afterthoughts. On-chain identity and permissioning are on track to become a core trust layer of the digital economy, not an optional layer of security bolted on at the end.
That idea is controversial. Some in the crypto industry insist that decentralized identity has already failed to gain real-world traction and that enterprises will ultimately gravitate toward familiar centralized credentials and private APIs. Others argue that AI agents are still experimental toys, far away from meaningful economic autonomy, so there is no urgency to redesign infrastructure.
Both perspectives miss how quickly reality is changing-and how poorly current systems are equipped to manage the risks. The rate at which enterprises are integrating AI into core workflows far exceeds the pace at which traditional, centralized infrastructure can adapt. The result is a widening gap between what AI agents are being asked to do and the trust mechanisms available to govern them.
Analysts are already quantifying this shift. According to Gartner, more than 40% of enterprise workflows will involve autonomous agents by 2026. This is not a distant sci‑fi scenario; it reflects what is already being deployed in fintech, logistics, procurement, and treasury. Increasingly, AI systems are not just suggesting what humans might do-they are clicking the button themselves, executing payments, reallocating balances, and interacting with financial markets.
At the same time, tokenization is moving from pilot projects to strategic initiatives at major banks, asset managers, and market infrastructures. As more financial instruments, cash equivalents, and real-world assets become tokenized, AI agents are being groomed to rebalance portfolios in real time, route cross-border payments, manage collateral, and optimize liquidity across rails that settle in seconds, not days.
Consumer behavior is evolving in parallel. A YouGov survey shows that 42% of US consumers would allow an AI agent to make purchases on their behalf if it guaranteed the lowest price. This signals a readiness to outsource everyday economic decisions to software, especially when framed as convenience and savings.
Security professionals see the other side of the equation. Research from Keyfactor indicates that 86% of cybersecurity experts believe autonomous systems should have their own unique, dynamic digital identities. In other words, the market is hungry for agent-driven automation, but the identity and trust frameworks needed to safely support that automation are nowhere near mature.
The real bottleneck is not how “smart” these agents are-it’s how verifiable they are. When an AI system initiates a multimillion-dollar treasury transaction, runs payroll, or interacts with decentralized finance protocols, we still lack a common standard to answer basic questions:
– Which organization authorized this agent?
– What scope of actions is it permitted to take?
– What risk profile or policy constraints apply?
– Who bears liability if it misbehaves or malfunctions?
Today’s default tools-API keys, shared secrets, static certificates-were designed for relatively passive software components, not for autonomous entities making high-velocity, high-stakes decisions. Static credentials don’t encode complex mandates, don’t travel well across systems, and are notoriously brittle when it comes to revocation and auditability.
This weakness is magnified on public blockchains. On-chain transactions are irreversible by design and largely pseudonymous. If an AI agent is managing tokenized assets, rebalancing positions across DeFi protocols, or directing stablecoin flows, its counterparties must be able to verify more than just the validity of its private key. They need cryptographic evidence of its authority, constraints, and provenance.
Blockchain-based identity infrastructures-built on verifiable credentials, decentralized identifiers, and programmable permissioning-offer a credible path forward. Such systems can allow an AI agent to prove, on-chain and without revealing unnecessary data, who issued its mandate, under what rules it operates, what limits apply, and how accountability is structured if things go wrong. In effect, they turn “dumb” addresses into rich, machine-readable representations of roles and responsibilities.
Critics worry that adding identity layers to on-chain systems will erode decentralization or turn blockchains into surveillance tools. Others claim that traditional, centralized identity providers are capable of solving the same problems with less complexity. Yet centralized credentials have serious drawbacks in the new environment. They lack transparency and auditability, are difficult to port across organizations and jurisdictions, and are poorly suited to the composable, multi-chain architectures that AI agents will increasingly navigate.
Enterprises are not blind to these tensions, but many remain hesitant. Executives often frame AI agents as experiments-labs projects rather than mission-critical infrastructure-even as they quietly deploy them in payments, treasury, trading, and procurement. At the same time, these same institutions are aggressively advancing tokenization, stablecoin-based settlement, and programmable compliance. The resulting architecture is incoherent: highly automated financial rails governed by ad hoc, opaque identity models that were never designed for autonomous agents managing billions in on-chain value.
The convergence of AI and tokenization is reshaping market structure itself. In some segments-market-making, liquidity provision, arbitrage, treasury routing-machine-driven actors will likely outnumber human traders, both in count and transaction volume. Without standardized Know Your Agent (KYA) frameworks, the system will fragment into trust silos, each with its own incompatible rules, and systemic vulnerabilities will multiply.
KYA is the natural extension of KYC and KYB into an era of autonomous software. Instead of just knowing your customer or business, you must also know the agents acting on their behalf. KYA means being able to answer, with cryptographic certainty:
– Which legal entity owns or controls this agent?
– Under what regulatory and contractual perimeter does it operate?
– What actions is it technically and legally permitted to perform?
– What safeguards, rate limits, and human-in-the-loop controls are embedded?
On-chain identity allows those answers to be encoded as verifiable, machine-readable facts, not buried in policy documents or access-control spreadsheets.
From a risk perspective, the stakes are high. Imagine an AI treasury agent instructed to optimize yield across tokenized money market instruments and DeFi pools. A bug in its optimization logic or a compromised credential could quickly turn into mass liquidation events, liquidity cascades, or unauthorized movement of customer funds. Without strong on-chain identity, attribution and remediation become slow, legalistic, and often futile-but the damage on-chain is instant and irreversible.
Similarly, as AI agents begin to interact with tokenized real-world assets-like tokenized funds, bonds, or invoices-regulatory exposure grows. Regulators will expect firms to demonstrate who authorized specific orders, which policies were applied, and how decision paths are audited. An on-chain identity layer for agents can bind actions to mandates in a way that is both regulator-friendly and privacy-preserving, reducing the industry’s reliance on opaque internal logs that are difficult to reconcile across organizations.
Building this identity stack is not just about slapping a name tag on an agent. It requires a layered architecture:
1. Decentralized identifiers (DIDs) or equivalent primitives to represent agents and their controlling entities in a globally resolvable way.
2. Verifiable credentials issued by trusted parties (employers, regulators, auditors, banks) to describe roles, permissions, and risk attributes.
3. Programmable policy engines that interpret these credentials on-chain, enforcing what an agent can or cannot do at the protocol level.
4. Revocation and rotation mechanisms that allow mandates to be updated or withdrawn in near real-time without halting entire systems.
5. Audit and attestation frameworks that allow regulators, counterparties, and insurers to verify that policies were actually enforced.
Some fear that such a system inevitably leads to hyper-regulated, closed ecosystems. That outcome is not inevitable. Properly designed, decentralized identity for AI agents can preserve pseudonymity for low-risk interactions, while enabling stronger, attributable identities where regulation or risk profiles demand it. Permissionless innovation and robust accountability are not mutually exclusive-they simply have to be separated at the architectural level.
There is also a powerful business incentive to get this right. Firms that can prove, with cryptographic evidence, that their AI agents operate under strict, transparent controls will enjoy lower compliance friction, better insurance terms, and higher trust from counterparties. In a world where agents transact at machine speed, counterparties will gravitate toward those ecosystems where identity and responsibility are legible rather than opaque.
Insurance and capital markets will likely accelerate this trend. Underwriters will demand standardized KYA data before pricing risk for AI-driven trading desks, autonomous treasury software, or agent-based payment systems. Rating agencies and risk assessors will look at how thoroughly identity and authorization are encoded into infrastructure, not just internal governance documents. On-chain identity for agents will cease to be a niche technical issue and become a pricing parameter in global finance.
Developers of AI systems will feel the shift as well. Instead of treating identity and authorization as burdensome compliance overhead, they will be forced to design with them as first-class features. Agents will ship with embedded identity stacks, capable of presenting different sets of credentials depending on context-one for internal corporate actions, another for cross-border payments, another for interaction with public DeFi protocols. Tooling that makes this composable and developer-friendly will become a competitive moat.
For policymakers, the emergence of AI agents with on-chain identity opens new possibilities. Rather than relying solely on ex-post enforcement, regulators can engage with standard-setting for KYA, endorsing minimal baseline requirements for agents that handle customer funds, securities, or critical infrastructure. Policy can become more granular: low-stakes consumer automation could enjoy lighter requirements, whereas systemically important agents might be subject to stricter credentialing and attestation.
The alternative-allowing AI agents to proliferate across tokenized markets without a coherent identity layer-is a recipe for repeated crises. We would be building a high-speed, always-on financial system where no one can reliably prove which machine did what, under whose authority, or according to which policies. That is not just a technical gap; it is a structural weakness that adversaries, rogue insiders, and faulty models will inevitably exploit.
The trajectory is clear: AI agents are becoming core economic actors, and tokenization is transforming how value moves and is represented. The intersection of these trends will define the next decade of digital finance. To navigate this transition safely, the industry must treat on-chain identity and Know Your Agent frameworks as foundational infrastructure, not optional polish.
AI agents will not “run wild” if we give them rigorous, verifiable identities, transparent mandates, and enforceable constraints encoded into the very rails they operate on. Without that, we are inviting them to do exactly that-at unprecedented scale and speed, in markets that increasingly have no off switch.