The FinTech company announced Wednesday (May 27) the launch of Agentic Trading and the Agentic Credit Card, which allows artificial intelligence (AI) agents to make trades and credit card purchases on a customer’s behalf.
“Our mission has always been to democratize finance for all, and now, that mission extends to AI agents,” Vlad Tenev, CEO of Robinhood, said in a news release.
According to Robinhood, Agentic Trading can come up with strategies based on a user’s investing and trading goals. For example, a long-term investor can have the agent analyze their portfolio for “concentration risk and sector exposure,” point out “where they’re over or underweight, and execute a rebalance.”
Meanwhile, Agentic Credit Card — which Robinhood says is one of the first products of its kind — lets agents spend on a customer’s behalf by linking them to Robinhood Banking’s MCP server.
“During setup, you’ll connect your agent to a dedicated virtual Robinhood Gold Card, set a specific spending limit that only you control and choose whether to require manual approvals or not,” the release said. “By default, agents are restricted to that individual virtual card only, with no access to your primary credit card number or any other Robinhood account information.”
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In an interview with The Wall Street Journal about these new offerings, Robinhood Vice President of Product Management Abhishek Fatehpuria said they are part of the company’s ongoing introduction of AI into its services.
“These AI agents for consumers have started to trade in the market,” Fatehpuria said. “One thing that we’ve learned from talking to our customers is that they want to give their agents the power of Robinhood, but in a very safe way.”
In related news, PYMNTS wrote Tuesday (May 27) that the rise of agentic AI will present a test to commerce systems, which “will increasingly be judged not simply on whether they block bad activity but whether they recognize good activity with sufficient confidence to let it proceed.”
Recent research from PYMNTS Intelligence suggests that AI adoption is coming together through ordinary, repeatable consumer behavior and not high-profile use cases. The report, “The AI On-Ramp: Data Shows How Everyday Tasks Build Consumer Habits,” contends that widespread adoption may depend on frequent, low-stakes tasks that establish durable routines.
“The report identified four characteristics of successful AI on-ramps: frequency, immediate utility, low stakes and broad demographic relevance,” PYMNTS wrote. “Across surveyed activities, finding product links emerged as the strongest universal use case. Survey data showed product discovery reached 29.8% adoption among AI users and continued gaining momentum into the current year.”
