Silent success is worse than a loud agent crash

An engineer warns that AI agents failing silently—returning a 'done' status without actually executing tasks—is a more costly failure mode than loud crashes. The developer advocates for shifting focus from sandbox isolation to reconstructing exactly what commands ran, their arguments, timing, and outcomes, to catch cases where agents appear successful but did nothing.

An agent that fails loudly is annoying. An agent that says "done" while nothing ran is expensive. I've been building with AI agents near real systems, and the failure mode I worry about most isn't a crash — it's silent success. The agent returns something that looks fine. The UI says the task completed. But when you dig in, the command never ran, the wrong thing ran, or it checked its own assumption instead of the actual system state. Isolation still matters — I wouldn't run arbitrary agent code without boundaries. But a sandbox alone doesn't answer: "did the thing I care about actually happen?" That's where I've shifted my thinking: less "is it isolated?" and more "can I reconstruct what executed — command, args, when, and outcome?" Curious: if you've shipped agents near real systems, how do you catch the "looks fine, did nothing" cases?