Agentic trading is trading in which an AI agent researches the market, decides on an order, and places it on a person's behalf, inside limits that person sets in advance. Robinhood's agent product can "scan the market, review your portfolio, build watchlists, trade, and more," with trade approvals turned on by default (Robinhood Agents). Onchain, the same idea runs through wallets and smart contracts instead of a broker account.
​

The useful question for anyone building or using an agent is not whether the model is clever. It is where the limits live and what happens when the agent tries to step outside them. This article covers how agentic trading works, the main setups, and the control layers that decide whether a bad decision becomes a bad trade.
​

Agentic trading is the use of an AI agent that can both decide and act in a market. A trading bot follows fixed rules that a person sets. An agent reads data, reasons about it with a language model, chooses an action, and calls a tool that submits the order. The person's job shifts from placing orders to setting the boundaries the agent works within.
​

Most agent products combine three pieces: a model that interprets goals and data, a set of tools (market data, portfolio reads, order submission), and an account the agent can trade from. Robinhood's version lets users "choose your model, set your controls," and fund a dedicated account that the agent trades from (Robinhood). Crypto analytics firm Nansen describes agentic trading as a shift from manual execution to delegated execution (Nansen).
​

An AI agent places a trade in four steps: it gathers data through tools, the model proposes an action, a policy layer checks the action against the user's limits, and an execution layer submits the order. The policy and execution layers matter most, because they decide whether a proposed action can actually move money.
​

In a brokerage setup, the policy layer is the broker's controls: a separate funded account and an approval step before each order. Onchain, an agent usually holds or controls a wallet and builds transactions directly. The open-source Solana Agent Kit, for example, gives agents tools for actions such as trading tokens and lending through protocols like Jupiter and Orca (Solana Agent Kit on GitHub). Frameworks like elizaOS describe agents that "trade onchain" through plugins (elizaOS docs). For more on how agents hold funds, see agent wallets.
​

Agentic trading setups fall into three groups: broker-hosted agents that trade inside a regulated brokerage account, exchange or bot-platform agents that trade through API keys, and onchain agents that sign blockchain transactions from a wallet. Each puts the spending limits in a different place, which changes how much a single bad decision can cost.
​

Trading limits work best when they sit at the point of execution, not only inside the model's instructions. A prompt that says "never spend more than a set amount" can be ignored, misread, or overridden by injected text. A limit checked by the account, wallet, or transaction itself holds no matter what the model decides.
​

There are three common places to put limits. The first is the account: fund a separate account with only what the agent may use, as Robinhood's dedicated account does. The second is approval: require a person to confirm each trade, which is safe but slow. The third is the transaction: write the conditions into the transaction so it cannot settle outside them.
​

Eco's Sauce SDK takes the third approach for onchain trades. A Sauce program reads live onchain state, checks conditions such as a minimum amount received, and reverts the whole transaction if they fail. The launch post frames it as setting hard boundaries for agents: "If they hold, the transaction completes. If they don't, nothing moves" (Eco Sauce launch post). That rollback covers one transaction on one chain; earlier transactions, such as approvals, are not undone (Sauce docs). See AI agent spend controls for the broader control stack.
​

The main risks of agentic trading are model error, manipulated inputs, stale data between decision and execution, and runaway loops that repeat a bad action. None of these depend on how capable the model is. They depend on whether the account, wallet, or transaction stops an action that breaks the rules the user set.

Readers asking what happens when this goes wrong onchain can read when an AI agent sends crypto by mistake and security risks of autonomous AI agents.
​

Agentic trading differs from a classic trading bot in who writes the decision logic. A bot runs rules a person wrote, such as buying a fixed amount every week. An agent decides what to do from goals and data. That flexibility is the appeal, and it is also why the agent needs firmer limits than a bot does.
​

Many traders combine the two: a rule-based program handles the repeatable part, and an agent handles research and suggestions. A dollar-cost averaging bot is the clearest rule-based example. For the payment side of agents, see agentic payments.
​

Robinhood agentic trading works by letting a user build an agent inside the Robinhood app, pick the model it runs on, fund a dedicated account for it, and decide whether each trade needs approval. Robinhood says the agent "comes ready to scan the market, review your portfolio, build watchlists, trade, and more," and that users can "fund a separate account for your agent to trade from" (Robinhood Agents).
​

Three controls on that page are worth reading closely, because they map onto the control layers any agent setup needs:

Robinhood also lists Agent Loops, which would let an agent "research and trade continuously or on a schedule," as coming soon (Robinhood). Continuous operation is where approval-by-default stops scaling: an agent that runs every hour cannot wait for a person each time, so the account and transaction limits have to carry more of the weight.
​

Setting up an agentic trading workflow comes down to six decisions made before the agent places its first order. The same sequence applies whether the agent runs in a brokerage app, through an exchange API, or onchain.

Agentic trading works as a way to delegate execution, research and routine portfolio tasks. Whether it makes money is a separate question that depends on the strategy, the market and the limits, not on the word "agent." An agent can follow a sensible plan more consistently than a person who is busy or tired. It can also follow a bad plan, or misread a good one, faster than a person would.
​

A fair way to judge an agentic trading setup is to ask what it does well and where it needs guardrails:
​

The honest answer for most users: start with research and alerts, add low-stakes orders with approval on, and expand only after reviewing what the agent proposed and what it was stopped from doing.
​

LLM trading means using a large language model to read information and propose trades. Language models are good at reading text, such as filings, news, governance posts and social feeds, and turning it into a summary or a view. They are weak at precise arithmetic, at remembering exact numbers across a long session, and at telling a real source from a convincing fake.
​

That split suggests a design. Let the model do what it is good at: reading, summarizing, and proposing an action in a structured form such as "swap token A for token B, size X, minimum received Y." Then hand the structured proposal to deterministic code that checks it against hard rules and builds the transaction. The model never touches the key directly, and it never gets to decide whether its own proposal is within limits.
​

Common failure modes of LLM trading that this design catches:

AI agent crypto trading differs from brokerage agents in one important way: onchain, the agent can build and sign transactions directly against protocols, so there is no broker sitting between the agent and the market. That removes a layer of protection and adds a layer of control, because conditions can be written into the transaction.
​

Solana Agent Kit's swap example shows the shape of this. The trade call takes the target token, the amount, the source token and a slippage parameter, and the kit handles "transaction generation, signing and sending, using provided wallets" (Solana Agent Kit on GitHub). The slippage parameter is a per-call guard; it only helps if the code that calls the tool sets it to a sensible value and the model cannot change it.
​

Programs that run inside one transaction take the guard further. With Sauce, the conditions are part of the program: read the live price, check it against a floor, require a minimum received, and revert if any check fails. Sauce's recipe list includes an oracle-guarded swap using Chainlink, a multi-venue route with per-swap minimums, and stop-loss and buy-the-dip programs (Sauce recipes). Those automation programs evaluate their conditions only when something runs them, so a scheduler or agent still decides when to call them; the program decides whether the trade is allowed to settle.
​

Yes. Onchain toolkits such as Solana Agent Kit give agents tools to swap tokens, and agent frameworks like elizaOS support onchain plugins. The agent needs a wallet it controls, so the key question is what limits apply to that wallet.
​

It depends on the product. Robinhood turns trade approvals on by default. Onchain agents only ask for approval if the developer builds that step in.
​

No. Algorithmic trading runs fixed rules. Agentic trading lets a model choose actions, which needs execution-level limits.
​

It is Robinhood's agent product: users choose a model, set controls, fund a dedicated account, and let the agent research and trade, with trade approvals on by default.
​

It is worth trying for research and routine execution with small amounts and approvals on. Treat returns as a property of the strategy, not of the agent.
​

Robinhood lists external agent access through MCP. Other brokers and exchanges offer APIs. Check what permissions the connection grants before connecting it.
​

Fund a small separate account or wallet and keep approvals on until you have reviewed enough of the agent's proposals to trust its pattern.
​

Sources: product and documentation pages linked inline, accessed November 2026.
​