AI Agents Process 6.4M Payments but Trust Gap Persists for Institutions

In May 2026, a publicly posted message containing Morse-coded instructions triggered a $175,000 payment from an AI-linked wallet. About 80% of that amount was later retrieved. The on-chain transfer was legitimate, but the software had followed directions from an untrustworthy source.

The event highlights a key challenge for autonomous finance: how can businesses trust AI agents to handle significant sums?

BeInCrypto Intelligence's State of AI Agent Payments 2026 report recorded 6.4 million x402 payments across Base and Solana over 35 days, totaling just $119,947. Over 90% of those transactions were less than one cent.

BeInCrypto spoke with five industry leaders about the future of agent payments.

Millions of Payments, Little Money

x402 allows software to automatically pay for online resources. However, its transaction volume does not necessarily indicate strong commercial demand. Some payments could involve testing or recurring service calls.

“Micropayments could remain the majority of transactions while trading and treasury operations account for a growing share of the value moved,” said Amir Forouzani, co-founder and CEO of Puffer Labs.

Saad Naja, founder and CEO of Hygentra, anticipates that AI-driven trading will grow considerably faster.

“I expect the next 12 months to be a breakout year for agent trading, although micropayments will still dominate transaction count. In 5 years I expect 80% of trading will be agentic,” he said.

That prediction is far ahead of current data. Across four chosen Hyperliquid connections, applications labeled as AI represented only 0.55% of trading value. Unlabeled AI activity could not be measured.

The Most Dangerous Payment Can Be Legitimate

The report found two incidents where $281,200 was sent to attacker-controlled wallets before recoveries. Neither case required breaking a blockchain signature. The agents acted on commands they should have declined.

“A securely stored private key can still authorize a disastrous transaction if the agent has been manipulated by a malicious website, invoice or response from another service… Spending limits alone are insufficient. You also need to control whose instructions the agent accepts and enforce its permissions independently of the model,” Forouzani warned.

One agent's error could also propagate across many others using shared data.

“When thousands of agents buy the same inexpensive feed and act on it at machine speed, one bad input gets repeated across all of them at once, and a spending cap set per agent does very little to contain that,” said Markus Levin, co-founder of XYO.

A Transaction Can Be Reversed After an Agent Acts

Benedikt Bünz, co-founder and chief scientist at Espresso Systems, says agents face another risk when they spend money before an underlying transaction is finalized.

“Ethereum transactions are only final after 16 minutes, i.e., they could be reverted before that. Agents either need to sit idle for that time or make their next move on a payment that could still be reversed. This exposure can have dangerous trickle-down effects at scale,” he said.

Automated trades and payments often rely on prior transactions. A reversal can impact subsequent steps. Ethereum's finality process demonstrates why confirmation and settlement matter.

Who Takes Responsibility When AI Gets It Wrong?

In the report's readiness framework, micropayments scored 60.7 out of 100, while institutional automation scored 12.0. Limited disclosure of real-world results contributed to the low institutional score.

Of 19 products reviewed, the 14 that handled money stayed within human-defined limits.

Chandler Fang, co-founder of t54 Labs, sees an unresolved question:

“Now agents have the technical capability to move money around and sometimes make their own decisions. How do we make sure that when agents make the wrong transactions, there are clear procedures and protocols to handle the downside risk?”

Levin expects the next big opportunity to come from payments tied to the physical world.

“The next category of agentic payments I’d expect to grow is payments released once work is done in the physical world, such as a delivery being completed or a job finished on site, and those carry much larger amounts than an API call,” he said.

Such payments require proof of task completion, permission rules enforced outside the AI model, and clear liability when a transaction fails. Institutions still need evidence that those safeguards function at scale.