Research-First Agents, Brokers as Backend

This shifts value away from the pipe that places the trade and toward the interface that forms conviction. In Artemis’s model, the investor starts with a thesis, the agent turns that into models, targets, and monitoring rules, and the broker handles account access, custody, and order routing in the background. That makes brokerage execution more interchangeable, while making the research workflow and agent experience the real point of product differentiation.

• The concrete workflow is thesis first, trade second. Artemis describes agents that pull data across public stocks, private companies, tokens, and prediction markets, then connect to brokers like Robinhood, Coinbase, or Schwab only when it is time to place or update an order.
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• This is already becoming product reality. Robinhood’s Agentic Trading lets third party AI agents connect through its Trading MCP to read account data and place trades in a dedicated account, which is exactly the kind of broker as back end execution layer described here.
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• Brokerages still matter, but more for who they serve and what they bundle around execution. Artemis frames Robinhood as skewing toward active traders, while firms like Schwab, Fidelity, and Interactive Brokers fit buy and hold investors, and Kraken and Coinbase are broadening across more asset types to keep capital on platform.
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The next battleground is not who can place an order, but who can turn a messy market view into a trusted automated portfolio action. As more brokers expose agent connections and more assets trade in the same account, the winning products will package research, monitoring, and investor specific workflows on top of commodity execution rails.