Public is launching AI trading agents that execute automated strategies on prediction markets through a partnership with Kalshi. The New York-based investing app allows users to instruct AI agents to trade on real-world events or use prediction market data to inform broader portfolio decisions. Users set a strategy and the AI converts it into fixed rules, ranging from sending alerts when odds change to automatically buying or selling investments. The agents can monitor markets continuously and execute strategies investors might not physically manage themselves. Public must approve all rules before agents can act. The platform aims to prevent unintended trades by converting requests into visual plans requiring user review before activation as fixed code. The product targets active, self-directed investors wanting to track events affecting portfolios, such as Federal Reserve rate decisions or regulatory approvals. Public, founded in 2019 and backed by Accel and Tiger Global, manages billions in assets across millions of users.

The future of Wall Street is here as startups and brokers build AI agents to trade 24/7. Jul 28, 2026 - 21:17 Imagine telling an AI agent how much risk you're willing to take, your retirement goals and when your kids will start college - then letting it manage your portfolio while you sleep. That vision of agentic trading, in which artificial intelligence doesn't just recommend investments but carries them out, is moving from concept to reality. Brokerages, startups and even retail investors are building AI agents that can help oversee portfolios and automate investing tasks once handled by humans. "Effectively everybody has their own family office that is working 24/7 for them while they're awake or sleeping," said Devin Ryan, head of financial technology research at Citizens. "This isn't 10 years away. This is coming in the next few years." Ryan believes those agents will eventually do much more than buy and sell securities. He envisions AI continuously managing taxes, cash balances, borrowing, mortgages and investment portfolios - all tailored to an investor's financial goals. Fully autonomous investing remains a work in progress, but the race to build it is already underway. Building the future. Rather than trying to create fully autonomous trading systems overnight, many firms are taking a gradual approach. Startup Podium Markets AI is among those building AI specifically for investing. Its assistant, Ivy, analyzes a customer's portfolio across multiple brokerage accounts and generates recommendations based on the investor's goals and risk tolerance. But it stops short of acting on its own. Users still decide whether to follow the recommendation and execute the trade themselves. "The AI informs, but the human decides," said Dirk Mueller-Ingrand, co-founder and CEO of Podium Markets AI. "The average investor still should be very much in charge of the final decision... We're going down the path of a persistent AI finance or trading buddy who's always with you." Larger brokerages are moving in the same direction. Robinhood in May introduced tools allowing third-party AI agents to connect with customer accounts. Brokerage firm Public, meanwhile, is developing AI agents in-house that can automate investing workflows within its platform. "What this era of agentic is doing... it goes away from just being able to research something by yourself and then make up your own ideas and then trade the way you've traded where it's now becoming automated and where AI agents can actually execute investment strategies on your behalf," said Leif Abraham, Public's co-founder and co-CEO. Ryan estimated agentic finance could increase transaction volumes by at least tenfold. A retail investor who currently trades roughly twice a month could eventually trade 20 times a day under an agentic model, he said. "By the end of next year, we think that on some of these platforms, the majority of transaction activity by number of trades will be done by agents, if you can believe that," Ryan said. From ChatGPT to investing agents. While Wall Street is building agentic investing tools, retail investors have spent the past three years testing what general purpose AI can do. Since ChatGPT burst into the mainstream in late 2022, many investors have used AI tools such as ChatGPT and Anthropic's Claude to summarize earnings reports, research companies and generate stock ideas. The results have been mixed, with some users treating AI as a research assistant while others have found it unreliable for making investment decisions. Obioha Okereke, a 29-year-old technology consultant in Georgia and founder of the financial literacy platform College Money Habits, built an agent using Claude to search for undervalued stocks and options opportunities. "It was essentially just asking Claude to act as a hedge fund analyst to find undervalued stocks," he said, adding that he still reviewed every recommendation before placing a trade. "I will always stand by AI being a tool as opposed to a replacement." Thomas Schlossmacher, a 31-year-old retail investor and founder whose company Specialty Tokens builds AI systems for businesses, tested a trading agent after seeing claims online that AI could uncover profitable market patterns. Instead, he said he "was just losing money consistently." "I think if you're using it for an automated system or relying on an agent to do it for you, you probably want a professional," he said. "To blindly give an agent and say, 'Hey, make me money,' I think is kind of dumb." Building guardrails. The debate highlights one of the industry's biggest challenges. Teaching an AI agent to buy or sell a stock is relatively straightforward. Teaching it what an investor actually means is much harder. An investor might simply tell an agent to "grow my portfolio aggressively." But does that mean taking on more volatility, concentrating holdings, using options or accepting a greater chance of loss? An AI agent can faithfully follow instructions and still produce an outcome the investor never intended. That's why many firms are building guardrails before giving AI greater authority. Public, for example, requires users to review and approve an agent's workflow before it carries out any investing tasks. "You still have the last word," said Abraham. "The AI agent will not have its own mind... It will only execute." The more responsibility AI agents assume, the more important it becomes for firms to ensure the technology behaves as intended. "You have to make sure that the customer's best interests are at the forefront," said Citizen's Ryan. "If the agent is not behaving as modeled or as you expect, that becomes a risk for the firm."

The future of Wall Street is here as startups and brokers build AI agents to trade 24/7. Imagine telling an AI agent how much risk you're willing to take, your retirement goals and when your kids will start college - then letting it manage your portfolio while you sleep. That vision of agentic trading, in which artificial intelligence doesn't just recommend investments but carries them out, is moving from concept to reality. Brokerages, startups and even retail investors are building AI agents that can help oversee portfolios and automate investing tasks once handled by humans. "Effectively everybody has their own family office that is working 24/7 for them while they're awake or sleeping," said Devin Ryan, head of financial technology research at Citizens. "This isn't 10 years away. This is coming in the next few years." Ryan believes those agents will eventually do much more than buy and sell securities. He envisions AI continuously managing taxes, cash balances, borrowing, mortgages and investment portfolios - all tailored to an investor's financial goals. Fully autonomous investing remains a work in progress, but the race to build it is already underway. Building the future. Rather than trying to create fully autonomous trading systems overnight, many firms are taking a gradual approach. Startup Podium Markets AI is among those building AI specifically for investing. Its assistant, Ivy, analyzes a customer's portfolio across multiple brokerage accounts and generates recommendations based on the investor's goals and risk tolerance. But it stops short of acting on its own. Users still decide whether to follow the recommendation and execute the trade themselves. "The AI informs, but the human decides," said Dirk Mueller-Ingrand, co-founder and CEO of Podium Markets AI. "The average investor still should be very much in charge of the final decision... We're going down the path of a persistent AI finance or trading buddy who's always with you." Larger brokerages are moving in the same direction. Robinhood in May introduced tools allowing third-party AI agents to connect with customer accounts. Brokerage firm Public, meanwhile, is developing AI agents in-house that can automate investing workflows within its platform. "What this era of agentic is doing... it goes away from just being able to research something by yourself and then make up your own ideas and then trade the way you've traded where it's now becoming automated and where AI agents can actually execute investment strategies on your behalf," said Leif Abraham, Public's co-founder and co-CEO. Ryan estimated agentic finance could increase transaction volumes by at least tenfold. A retail investor who currently trades roughly twice a month could eventually trade 20 times a day under an agentic model, he said. "By the end of next year, we think that on some of these platforms, the majority of transaction activity by number of trades will be done by agents, if you can believe that," Ryan said. From ChatGPT to investing agents. While Wall Street is building agentic investing tools, retail investors have spent the past three years testing what general purpose AI can do. Since ChatGPT burst into the mainstream in late 2022, many investors have used AI tools such as ChatGPT and Anthropic's Claude to summarize earnings reports, research companies and generate stock ideas. The results have been mixed, with some users treating AI as a research assistant while others have found it unreliable for making investment decisions. Obioha Okereke, a 29-year-old technology consultant in Georgia and founder of the financial literacy platform College Money Habits, built an agent using Claude to search for undervalued stocks and options opportunities. "It was essentially just asking Claude to act as a hedge fund analyst to find undervalued stocks," he said, adding that he still reviewed every recommendation before placing a trade. "I will always stand by AI being a tool as opposed to a replacement." Thomas Schlossmacher, a 31-year-old retail investor and founder whose company Specialty Tokens builds AI systems for businesses, tested a trading agent after seeing claims online that AI could uncover profitable market patterns. Instead, he said he "was just losing money consistently." "I think if you're using it for an automated system or relying on an agent to do it for you, you probably want a professional," he said. "To blindly give an agent and say, 'Hey, make me money,' I think is kind of dumb." Building guardrails. The debate highlights one of the industry's biggest challenges. Teaching an AI agent to buy or sell a stock is relatively straightforward. Teaching it what an investor actually means is much harder. An investor might simply tell an agent to "grow my portfolio aggressively." But does that mean taking on more volatility, concentrating holdings, using options or accepting a greater chance of loss? An AI agent can faithfully follow instructions and still produce an outcome the investor never intended. That's why many firms are building guardrails before giving AI greater authority. Public, for example, requires users to review and approve an agent's workflow before it carries out any investing tasks. "You still have the last word," said Abraham. "The AI agent will not have its own mind... It will only execute." The more responsibility AI agents assume, the more important it becomes for firms to ensure the technology behaves as intended. "You have to make sure that the customer's best interests are at the forefront," said Citizen's Ryan. "If the agent is not behaving as modeled or as you expect, that becomes a risk for the firm."