An Answer Is Free to Be Wrong, an Action Is Not

For two years, the worst outcome a system could produce was delivering incorrect information. Readers would doubt the content, verify its accuracy, and learn to scrutinize subsequent data more carefully. However, the system has now evolved to become more proactive. It no longer merely provides answers; it executes commands, opens pull requests, runs API calls, updates databases, and posts messages. The type of error has shifted from a mere misstatement to a potentially damaging action.

When a system produces a wrong action, the damage is already done. There's no chance to verify or challenge the outcome retrospectively. The impact is immediate and irreversible. This change in the system introduces a new level of responsibility and accountability. The user can no longer simply read the system's output skeptically afterwards. Instead, they must now assume the system's actions as the new reality.

The system's new autonomy also introduces a new kind of pressure. It works in discrete steps, each accompanied by a button that signals the next action. At first, the first eight steps are handled correctly. However, by the fortieth step, the user's brain begins to recognize patterns and may begin to overlook the details. This could lead to a false sense of security and complacency. It's not carelessness, but rather the natural effect of prolonged exposure to nearly identical tasks.

To mitigate this risk, it's suggested to separate the proposing and applying stages. The system should be allowed to work its magic in a controlled environment, such as a separate branch or a test environment, before any changes are applied to the main system. This approach allows for a thorough review and testing of the proposed changes. It's like having a dry run before the real show.

The key is to give the system enough autonomy to do its job, but not too much that it can't be audited. The credentials used by the system should be proportionate to the task at hand, not over-privileged. It's crucial to consider what happens when the system fails. Can the system be rolled back before anyone else notices the mistake? The system's reliability is no longer measured by how often it gets it right, but by how it handles the rare instances when it gets it wrong.