Agentic systems are AI-driven architectures in which software agents trade, lend, allocate, and execute financial decisions with limited human oversight, acting autonomously within delegated mandates. Agentic trading applies this to markets: agents place orders, manage risk, and rebalance portfolios on behalf of users rather than merely surfacing recommendations.
An agentic system pairs a large language model or planning agent with tools (brokerage APIs, credit rails, market data) and a delegated mandate, letting it decompose a goal into actions and execute them with minimal human approval. Unlike a chatbot that advises, an agent transacts — closing the loop from intent to filled order.
In 2025 retail platforms began productising autonomy: Robinhood announced agentic trading tools and an agentic credit card, signalling a shift from human-in-the-loop advice to machine execution. For macro watchers this raises questions about correlated failure modes, herding, and a price-insensitive flow layer if many agents share models, prompts, or data feeds.
In 2025, Robinhood unveiled agentic trading features and an agentic credit card, allowing AI agents to execute trades and manage credit decisions on a user's behalf with limited manual confirmation — a concrete move from recommendation engines toward delegated, autonomous financial action at the retail layer.