AI agents fail silently, resist replay

The thread lands on the unglamorous truth: agent reliability is a systems problem wearing a model-shaped mask. If you cannot replay a bad run, you do not have debugging, just forensic storytelling.

• –One commenter described a bug that kept running wrong for days before a user screenshot exposed it, which is exactly the nightmare class here: healthy-looking systems that are simply incorrect.
• –Reproducibility needs structured traces of prompts, tool calls, outputs, and permissions; otherwise a rerun tells you almost nothing.
• –The fixes people actually report are classic ops discipline: schemas, explicit error surfacing, audit trails, and smoke tests after every change.
• –The stack around the model is often the real culprit, with flaky APIs, browser timing, partial responses, and state drift masquerading as model bugs.
• –The winning pattern is replayable execution plus decision provenance, not more autonomy by default.