Logging pre-flight checks for AI agent transactions to ensure auditability

On November 9, 2026, DEV Community's AI channel published a hands-on post proposing an audit method for AI agent trades. The author starts from a practical gap: an agent places an order on Tuesday, and by Friday you want to know why it bought 12 shares instead of 40, or why a different order never went out at all. If the only record kept is the broker's order list, neither question can be answered, because the broker sees only what was actually sent. The proposed fix is to log the outcome of every pre-trade check in its own table, stored next to the order record. Approved orders and rejected ones each get a row, and every row carries the full JSON payload alongside the decision fields that produced the result, so the agent's reasoning can be traced and audited after the fact. The post includes working code, and the author notes that SQLite is sufficient when the setup serves a single agent, while the same column layout works unchanged in Postgres. The motivation is framed purely as operational transparency: when an order goes out, the record should preserve what the agent evaluated, what conditions it applied, and why it acted as it did. As of this writing the post stands alone, with no independent reproductions or public discussion from other developers recorded yet.