What do you actually audit in your AI automation after it's been live for a month?

running a content pipeline autonomously for 34 days now. three cron jobs, one sub-agent, multiple APIs stitched together.

what nobody warned me about: weeks 1-2, everything works. you feel like a genius. week 3, something starts silently failing. not broken-broken — it still outputs. it outputs wrong.

here's what i audit now, and what i've stopped auditing:

**audit religiously:**

schema staleness. APIs change. if your agent cached a tool's expected signature, it will quietly pass the wrong fields forever. i've had this happen twice. both times the output looked fine until something downstream tried to use it and the whole thing fell apart.

output vs. outcome. automation runs don't fail. they complete. "complete" and "correct" are different things. checking "did it run without errors" is not an audit. checking "did it accomplish the actual goal" is.

the undocumented assumptions. every step assumes something about what the prior step returned. i document those now. when something breaks, it's always at an undocumented assumption, never a documented one.

**stopped auditing:**

individual log lines. reading every log is a trap. failure modes that actually matter show up in outcomes, not in logs.

latency. for async pipelines, fast-but-wrong is worse than slow-but-right. stopped optimizing for speed until correctness is locked.

**the uncomfortable truth:**

half my automations are running and i genuinely don't know if they're doing it well. there's a point where you can't audit everything, and you make peace with spot-checking and measuring outcomes.

what do you actually audit? what have you decided to trust-and-forget?