How do you keep an AI trading agent from using future data by accident?

You keep an AI trading agent from using future data by accident by turning the idea into a repeatable decision rule, attaching realistic turnover and risk constraints, and checking whether the workflow still holds up once the flattering assumptions are removed.

In point-in-time data and agent safety, the useful version of this workflow is the one that survives a clear benchmark, realistic execution assumptions, and a portfolio context that does not quietly change the rules after the backtest is done.

Start by naming the decision the page is actually supposed to improve. Then turn the rule into something testable enough that a teammate could reproduce it without inheriting your hidden assumptions.

The key checks are whether the workflow still helps after costs, whether nearby settings behave similarly, and whether the resulting rule would still feel operable once it becomes part of a live research stack instead of a notebook experiment.