Should You Let AI Trade for You?

The vocabulary around AI trading is imprecise enough to be misleading. Signals, bots, and agents appear in the same conversations and sometimes interchangeably, but they describe three fundamentally different things. The difference determines what the AI can do without you.

Signals, bots, and agents aren't the same thing

A signal is deterministic and rule-based. A TradingView strategy that fires an alert when RSI crosses a threshold is a signal.

It does not decide anything. It checks a condition, and when the condition is met, it alerts. The logic is visible, auditable, and predictable before you deploy it.

A bot executes orders based on those signals using parameters you set in advance. A bot that buys 2 contracts of SPY when it receives a webhook, with a stop at a set level and a take profit at another, is deterministic execution on pre-set human parameters. The bot does not choose the strike, the size, or the timing outside of what you configured.

An agent is different. An AI agent uses a language model to reason about what to do.

You tell it to buy SPY exposure when conditions look favorable, and it decides what favorable means, which instrument to use, what size makes sense, and when to enter. The output is nondeterministic: the same prompt can produce different decisions on different runs. The agent is making live judgment calls, not executing pre-set rules.

Most retail automation tools are bots, not agents. When AI trading coverage talks about AI making live trading decisions, it is usually describing agents.

A real trade that went sideways

In July 2026, StockBrokers.com published a hands-on evaluation of every major US broker's MCP integration. The author connected Claude to an Interactive Brokers account and asked it to build a bull call spread on USO with a $140 target over two weeks.

Claude pulled live data: USO was at $127.66, implied volatility near 59%, the move to target represented about 0.8 standard deviations over the time period. That analysis was sound. It then compared two expirations: an Aug 14 weekly and an Aug 21 monthly, both pricing the 130/140 spread at nearly identical net debits of roughly $2.77 and $2.79.

Claude picked Aug 21. One reason was defensible: the later expiration had roughly eight times the open interest on the long leg, a legitimate liquidity argument.

The second reason was wrong. Claude described the later expiration as giving the trader an extra week of time for the same cost.

A bull call spread at your target price is worth less with more time remaining, not more, because the short leg still carries value. If USO had printed $140 on Aug 13, the Aug 14 spread would have been worth roughly $8.30 and the Aug 21 spread roughly $6.30.

The extra week was a cost, not a free upgrade. The AI presented it in the same confident voice it used for the sound parts of its analysis.

The draft never became an order. Interactive Brokers keeps the AI out of execution, so the trade landed in an AI Instructions tab for human review and was caught there.

How much control each broker gives up

The six brokers with first-party MCP servers have built very different safety architectures.

Interactive Brokers keeps the agent out of the order book entirely. Drafts land in a separate AI Instructions tab on any IBKR platform, where you review and submit them yourself.

The AI session has no execution path. Nothing ships without a human click, and there is no added cost or minimum to access the integration.

TradeStation requires explicit confirmation before any order is placed. The agent can reach equities, options, and futures, and its actions remain subject to the trading permissions already on your account.

No order reaches the market until you approve it. Webull recommends a preview step, but TradeStation enforces one. The gate to access is the highest here: $10,000 minimum eligible balance and a paid Claude Pro or ChatGPT Plus subscription.

Webull lets the agent place orders directly but gives you the most granular controls of the group. You can cap the dollar value of a single order, cap share quantity, and restrict trading to a whitelist of symbols.

A read-only mode disables the trading tools entirely. The local server defaults to Webull's sandbox rather than production, so you can run a full session before real money is involved.

Webull covers the widest range of instruments (equities, options, futures, crypto, and event contracts) and charges no commissions on stocks, ETFs, or options contracts.

Robinhood confines the agent to a dedicated agentic account, which limits how much damage a bad order can do to your primary portfolio. That containment applies to trading, not visibility.

Once connected, the agent reads every Robinhood account you hold: your primary account, your IRA, your full order history, your watchlists. There are no configurable dollar caps, no symbol whitelist, and no read-only toggle.

Robinhood's own disclosure states that agent strategies can move quickly and may be difficult to monitor or stop in real time.

Public is the loosest of the group. Its Agentic Brokerage product lets you describe a strategy once, confirm it once, and the agent executes from that point forward without alerting you before each transaction.

Public's own disclosure is direct: executed transactions cannot be reversed. The platform documents position maximums (10,000 shares or $5 million on equities, 1,999 option contracts), but those are venue caps, not guardrails you configure for your own agent.

Public has no paper trading environment to rehearse in before going live.

Alpaca gives the agent the broadest execution surface of the six: market, limit, stop, stop-limit, and trailing-stop orders across stocks, options, and crypto, plus the ability to close or liquidate positions and exercise option contracts directly. There is no documented confirmation step, dollar cap, or read-only mode. The agent can place an order the moment it decides to.

The one built-in safety rail is paper trading. Alpaca's MCP server works identically against a paper account funded with $100,000 in simulated money, and switching to live trading is a matter of swapping which API keys you hand it. That puts Alpaca closer to Public in practice than to TradeStation: whatever guardrails exist have to come from you, either by rehearsing thoroughly in paper mode or by building limits into how you prompt the agent.

Why options raise the stakes

Options are more exposed to AI reasoning errors than equities because the decision set is wider. With equities, the key question is whether to buy or sell. With options, the agent also picks expiration, strike, structure, and whether to hold to expiration or exit early.

Each is a compounding variable, and options pricing is counterintuitive in ways that catch experienced traders. The time value error in the USO example above is a real edge case, not a contrived one.

Multi-leg strategies add more surface area. An agent building a vertical spread has to get both legs right, and an error on one shows up only in the net debit or credit calculation, which the agent may or may not verify against the price it is actually proposing to pay.

Where RelayDesk fits on that spectrum

The guardrail question is not unique to MCP-connected agents. Any trading system sits somewhere on a spectrum from fully deterministic to fully agentic, and the risk at each point is roughly proportional to how much judgment the system exercises without a human review step.

RelayDesk's Relay AI Assistant sits at the deterministic end of that spectrum. It reads your trading data, analyzes your signal history, and can suggest changes to your bot configuration, but it cannot place, modify, or cancel a broker order under any circumstance.

Any configuration change it suggests requires a manual confirm click before it takes effect. The flexibility trade-off is purposeful: an assistant that cannot act on its own also cannot rebalance your portfolio while you sleep.

But the risk profile is clear. It is one of the few architectures in this category where the AI's access to your account is read-only by design, not by configuration.

That is a different design choice from what Public and Robinhood are offering: an agent that acts while you are not watching. Neither approach is wrong by definition. They are different answers to the same question.

The question that matters

Most coverage of AI trading focuses on whether the AI is good enough. That framing misses the point. Current AI agents, including frontier models from Anthropic and OpenAI, make errors.

They reason confidently toward wrong conclusions and carry context from earlier in a session in ways that distort later decisions. The USO example is not a worst-case scenario: it comes from a careful evaluation, using one of the most capable models available, on a trade the AI found plausible enough to draft.

The question is not whether the AI will make an error. The question is what your setup does when it does. Specifically, what can the AI do without a human in the loop at your broker or platform, and what stops a confident mistake from becoming a filled order?

That is the question worth having an answer to before connecting anything to a funded account.

Sources:

• StockBrokers.com, "Best Brokers for AI Trading Agents in 2026: MCP Tested" (Jessica Inskip, Jul 31, 2026), primary source for broker comparison and IBKR/Claude case study
• Robinhood Agentic Trading disclosures (robinhood.com/us/en/agentic-trading)
• Public MCP trading (public.com/mcp-trading)
• RelayDesk Relay AI Assistant documentation (relaydesk.io/docs)

RelayDesk is a trading automation platform. We are not a broker-dealer or investment advisor and do not provide investment advice. Trading involves substantial risk of loss. Past performance is not indicative of future results. View full disclaimer