AI trading agents are here. Should you let one trade for you?

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Who’s in control?

© charles taylor, Tatiana Shepeleva, Yeti Studio/stock.adobe.com; Photo illustration Encyclopædia Britannica, Inc.

By now, you probably know that artificial intelligence tools can help you analyze a chart, summarize an earnings call, test a trading theory, or handle any number of other, sometimes menial trading tasks. In other words, AI can make a great trading assistant. But ultimately, when it’s time to make a trade, the decision to push the button has been yours.

With an AI agent integrated into your brokerage account, though, you can give it instructions, allocate some money, ask it to monitor the markets and look for setups that align with your strategy, and execute trades while you’re off doing something else.

Is letting an AI agent execute your trades fundamentally different from using the automated trading systems that have existed for decades? And should you turn the keys over to your AI assistant and let it drive?

Computers have been making trades without human interaction for a long time. Algorithmic trading has been used by institutions for decades, and individual traders can create automated systems through brokerages and trading platforms.

With a traditional algorithmic trading system, predetermined rules tell the software what to do when specific conditions occur.

For example: “Buy shares of Apple (AAPL) if the stock closes above its 50-day moving average on at least two million shares traded. Risk no more than 1% of my portfolio. Sell if the stock drops more than 5% from my entry point.”

In this case, the software executes the rules exactly as laid out. It doesn’t try to interpret or question what you meant. The instructions are very black and white. If A happens, then do B.

Where an AI agent differs is how decisions are defined, who makes them, and when they are made. Instead of requiring a developer to anticipate and code every rule, an agent can interpret changing information, choose from possible actions, then act based on its understanding of what you’ve asked it to accomplish.

An algorithm automates the execution in a decision process; an agent can create parts of the decision process itself. This flexibility adds a powerful component to your trading, and that’s the attraction, but it’s also an additional risk.

Agentic trading is not some futurist’s version of what trading could become; it’s actually here today. Robinhood, an online brokerage, allows you to create AI agents either inside its app or connect outside agents to its platform.

Here are just a few of the roles an AI agent can take on your behalf:

Active trader. An agent can not only backtest a strategy against historical market data, but also determine how to translate ideas, theories, and approaches into testable rules. If the results look promising, it can implement the strategy. More importantly, it can revise and refine that strategy, not based on predetermined rules, but on the overall objectives.

Thematic investor. For investors who focus on specific themes, such as biotech or semiconductors, an agent can build a portfolio based upon specific guidelines, continually monitor the sector for developments and potential opportunities, and adjust holdings over time.

Long-term investor. An agent can review a portfolio for excessive concentration in individual stocks or sectors, identify positions that have grown too large or too small, and rebalance the portfolio to the desired allocation.

Brokerage pioneer Robinhood, which has moved aggressively into agentic trading, has put safeguards around the process. For example, your agent trades through a separate account that you fund with a dedicated amount of money.

Although your agent can see information from your other Robinhood accounts, it can only trade in the designated agentic account. It can’t tap those other accounts for additional trading capital. You can also require approval before each trade, monitor the agent’s activity, and pause automations—or disconnect it completely.

In theory, this means that if you put $5,000 in an agentic account, you’re not giving it free rein over the $500,000 in your individual retirement account (IRA). However, there’s an important distinction. These safeguards limit how much damage an AI agent can do, but they can’t make you a better trader.

This becomes clear when you read Robinhood’s own disclosures.

Robinhood is also explicit about what those safeguards can’t do. Its disclosures warn that AI agents can make mistakes, misunderstand instructions, act on incomplete or outdated information, and sometimes behave in unexpected ways.

Depending on how you configure trade approvals, an AI agent could execute a trade without asking you first. The company says it doesn’t guarantee the accuracy, completeness, or suitability of the agent’s output, and that ultimately, customers are responsible for monitoring their own accounts, positions, and trades.

If Robinhood’s agentic trading platform catches on, other brokerages are likely to follow, much as they did after the company popularized commission-free stock trading. And their agentic offerings will almost certainly come with similar disclosures. The point isn’t that Robinhood’s safeguards are inadequate; it’s that safeguards and sound trading decisions are two different things.

Think of the safeguards as guardrails. They’re placed along mountain roads to keep you from going over the cliff, but they can’t tell you if you’re driving in the right direction.

Advances in technology have changed every part of the trading process. Charts once drawn by hand now render instantly. Data and information that took hours or days to find, and sometimes required a trip to the library, are available online in seconds. And trade executions that required a phone call to your broker are now done with the push of a button on a mini computer in your pocket.

But the fundamental questions that determine whether a trading strategy is sound haven’t changed.

What’s a valid setup? How much are you willing to risk? Where will you take your loss? Where will you take your profit? What happens if you have three losing trades in a row? What happens when the market changes, and your strategy, which worked for the last six months, suddenly stops working?

An AI agent has to answer those same questions. And removing human emotion from the execution doesn’t necessarily remove it from the strategy.

Unlike humans, an AI agent never gets scared, greedy, bored, or angry. It’s not going to chase a stock because it feels like it’s missing out. It’s not going to double down on a losing position because it’s in a bad mood. And it won’t personalize a loss and try to revenge-trade to get back to even. But that doesn’t mean you’ve eliminated behavioral bias; you may have just moved it upstream.

If you tell an agent to be “aggressive,” what does that mean? If you tell it to find “great stocks at a good price,” whose definition of “great” or “good” does it use? If you build a strategy around flawed assumptions or your internal biases toward fear and greed, an AI agent can potentially execute your assumptions faster and more consistently than you ever could.

AI doesn’t turn a bad trading strategy into a good one; it just automates it.

The technology behind agentic trading is designed to take the process out of your hands. But ironically, the responsibility ends up right back where it started, with you. AI may be the shiny new toy, but one of computing’s oldest rules still applies: garbage in, garbage out. Give an agent a flawed strategy, fuzzy instructions, or your own baked-in biases, and it can execute them with remarkable efficiency.

You decide the strategy, how much money your agent gets, how much authority it has, and you have to monitor what it does. But if things go wrong, you own the losses. So instead of asking whether you can trust an AI agent to trade for you, it’s better to ask how much money you might be willing to put under its control.