Agentic trading is trading carried out by an AI system that is given a goal and works toward it autonomously — deciding when to act, how much to trade, and when to stop, rather than executing a fixed instruction.
The word “agentic” borrows from AI research, where an agent is a system that perceives its environment, makes decisions and takes actions over multiple steps to achieve an objective.
How it differs from a bot
A trading bot follows a rule: buy at this price, sell at that one, repeat. It has no goal beyond executing the rule, and it will execute the rule into a loss without noticing.
An agent has a mandate: build a position in this asset over the next day at good prices, or reduce my leverage before this becomes dangerous. It evaluates conditions and chooses actions, and it can decide that the right action right now is to wait.
What “autonomous” means in practice
Autonomy is scoped. An agent operates within a mandate a person defines — what to trade, how much, over what window, with what limits. Inside that mandate it makes decisions. Outside it, it doesn’t act. A well-designed agent is autonomous the way a portfolio manager with a written mandate is autonomous.
What it isn’t
A chatbot that gives trading opinions. An LLM can reason about markets in text; that’s not the same as an agent that observes prices, sizes a trade and executes it. Most products described as “AI agents” for trading are one or the other, and the difference matters.
Where the concept is heading
Toward a division of labour: models that predict, agents that act on the predictions within human-set constraints, and people who set the constraints. AlphaNet’s Copilot tools — Managed Entry, Managed Exit, Leverage Guard — are mandate-scoped agents in this sense.