New research has found that autonomous agents, software systems that plan, decide, and execute on-chain transactions without direct human input, now drive more than 19% of on-chain activity.

But while these agents have outperformed humans on narrow tasks, they still lose by up to a 5-to-1 margin in open-ended trading, according to a DWF Ventures report published Thursday.

In decentralized finance, these agents run yield strategies across lending protocols, manage liquidity, rebalance portfolios, and execute trades. Total value locked in agent-managed positions has climbed past $39 million, with most deployments still in early testing, per the study.

In one sample, autonomous finance protocol Giza's ARMA agent, which moves stablecoins between lending platforms to get the best rates, has earned users 9.75% a year, beating yields on other decentralized finance protocols such as Aave and Morpho, per the study.

That picture shifts, however, once the task at hand gets harder.

In a stock trading contest run by tradexyz, the top human beat the top agent by more than 5x. A separate contest between leading AI models, held by nof1, found that only three of seven were able to turn a profit per trade.

“Agents struggle when the situation isn't clearly defined,” but they thrive “when the objective is narrow and the parameters don't move often,” Xin Yi Lim, senior associate for investments at DWF Labs, told Decrypt.

This is one of the reasons why yield optimization, the practice of moving capital between lending protocols to capture the highest available returns, has become an early proving ground for agents, Lim explained.

"Agents thrive when the objective is narrow and the parameters don't move often, which is why yield optimization works,” Lim said. “Until agents can reason and adapt to real-time information, they will not be able to react when the market changes and conditions are unclear.”

Builders in the space appear to echo this concern.

An agent can be as capable as a human “if given all the context and tools," MoonPay chief engineer Neeraj Prasad told Decrypt in an interview. He warned, however, that "the writing is on the wall that agents are both more competent, harder working, and malicious in some cases."



Still early

The findings come as Ethereum developers work to make it easier for agents to handle complex on-chain tasks.

Earlier this month, a new standard that would let agents run several actions on decentralized finance protocols at once was proposed by decentralized relay network Biconomy and was backed by the Ethereum Foundation.

Industry leaders, meanwhile, are betting that autonomous agents will soon handle a far larger share of economic activity.

"The agentic economy could be larger than the human economy," Coinbase CEO Brian Armstrong tweeted Thursday, noting how it could drive demand for stablecoins beyond current estimates.

Researchers on the ground see a longer runway. Most of the 19% figure is bots doing narrow work like MEV capture and stablecoin routing, with true agentic activity still a minority share, DWF Labs’ Lim noted.

"A realistic timeline is five to seven years before agentic volume meaningfully rivals human volume in any major financial vertical, with on-chain getting there first because the infrastructure is more permissionless," Lim said.

Still, some see the current gap as a structural feature of where agents are today.

"Where they fall short is open-ended trading, which requires contextual reasoning, narrative awareness, and weighing unstructured information," Aytunc Yildizli, chief growth officer at decentralized AI infrastructure developer 0G Labs, told Decrypt.

Closing that gap, he added, would take more than better models.

“Users need cryptographic proof an agent did what it claimed, inside a trusted execution environment no one can tamper with, running on infrastructure that doesn't just move the trust assumption to a single cloud provider,” he said.

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