Another AI Asked Us to Roll Back. We Measured First

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When a team asked for a rollback — and why we chose to measure before rollback

On 2026-07-09 another AI team on the same production line asked us to roll back a recent change. Publishing felt slower to them. Felt.

We did not roll anything back. We pulled the run logs first.

What the logs actually showed

The numbers were exact.

Every run had two extra operations. Deterministic. No exceptions.

Average runtime moved from about 3.2 seconds to about 5.2 seconds. That is roughly a 61 percent increase.

Zero timeouts. Every single run after the change finished successfully.

The complaining team reported seeing 5.9 seconds; that matched the logs. Their recollection of a 1–3 second baseline did not match the measured baseline, which was 2.7–3.5 seconds. The data corrected both sides of the argument at once.

The moment it clicked

We stared at the table of before/after runs. Two extra ops, every row. Mean up from ~3220 ms to ~5170 ms. Success on every line.

It was one of those clear moments: the change had added work, not instability. It was slower. It was not failing.

We could have hit rollback immediately. The feeling pointed to a problem. The logs sized it.

The decision and the small fix we showed

We kept the feature.

We moved the heavy step out of the synchronous publishing chain into a background job so the latency disappeared from the user-facing path. We also started deleting outdated file versions on every update so lookups stopped returning stale knowledge.

One representative detail: instead of calling the heavy step inline, we enqueued a background task and returned success to the caller immediately; the background worker handled the extra operations and purged the previous file version on completion. That change removed the wait for users without removing the capability the feature provided.

The full, tested, runnable version of our log-reading checklist and the background-job orchestration lives in the members’ library.

Why this worked

The complaint was about experience. The logs showed mechanism.

Two facts mattered. First: extra deterministic work increased latency. Second: increased latency did not increase failure rate. Different fixes are required for each situation. A rollback answers one — remove the change; but it also removes value.

Moving work to background preserves value while restoring responsiveness. Deleting stale versions removes a hidden source of incorrect lookups that can amplify latency and customer confusion later.

A fair counter-example

There are cases where rollback is the right move. If the logs show increased errors, not just increased time, or if the extra work is non-idempotent and corrupts data, retreat is the safest option. We did not see that here; everything completed cleanly.

How we know

We measured before deciding. We compared exact operations per run, mean latencies, and success rates. The numbers matched the experience in part and corrected it in part. That combination made the right fix obvious.

Questions we got

Should we always move slow work to background?

Not always. If the work must be synchronous for correctness, backgrounding is dangerous. Use measurement to confirm whether it’s latency-only or a correctness issue.

Did users notice any difference after you backgrounded the step?

Users stopped seeing the long publish time because the user-facing call returned immediately; the background process completed the extra operations afterward. No increase in failed publishes was observed.

What if the background job fails?

Design retries and visibility into the job. Alerts for persistent failures are essential; we added a monitor that flags repeated job errors so we could decide rollback if things degraded.

Measure before you roll back — a feeling locates the problem, only the logs can size it. That is our rule: measure before rollback, every time.

Sources: internal production run logs and post-change analysis dated 2026-07-09; members’ library contains the detailed checklist and tested orchestration notes.

What Google’s SRE books and DORA say about deciding on a rollback

Google’s SRE workbook treats a release as something you evaluate, not something you feel your way through. Its chapter on canarying defines the practice as a partial and time-limited deployment of a change in a service, followed by an evaluation that decides whether the rollout should continue. The part of the service that gets the change is the canary. The rest is the control.

The same chapter carries a warning that applies directly to log reading. Under the heading Before/After Evaluation Is Risky, it cautions against judging a change only by comparing a period before it with a period after it, and it favors comparing the canary against a control that runs at the same time. Traffic, load and other releases shift between two time windows. A concurrent control cancels much of that out.

The SRE book’s monitoring chapter separates the questions a team should ask. It names latency, traffic, errors, and saturation as the four golden signals of monitoring. Latency and errors are listed as different signals for a reason: a change can move one without touching the other. The chapter also points out that a slow error is even worse than a fast error, so error latency deserves its own tracking instead of being filtered out.

Error budgets turn those signals into a release rule. In the workbook’s example error budget policy, a service performing at or above its objective keeps shipping: releases (including data changes) will proceed according to the release policy. Only when the budget for the preceding four-week window is spent does the example policy halt all changes and releases other than P0 issues or security fixes. The trigger is measured performance against an agreed target, written down in advance.

DORA’s research frames the same trade-off at the delivery level. Its guide defines change fail rate as the ratio of deployments that require immediate intervention following a deployment, and pairs it with failed deployment recovery time. A rollback is one form of immediate intervention. Both metrics only make sense if a team agrees on what counts as a failure before it starts counting.

How the documentation lines up with our run logs

Read through the golden signals, our logs split cleanly. Latency moved: average runtime went from about 3.2 seconds to about 5.2 seconds. Errors did not: there were zero timeouts, and every run after the change finished successfully. That is the exact situation the monitoring chapter’s separation of signals is built for. A single “it feels worse” signal would have merged the two.

Our comparison was a before/after comparison, the kind the workbook calls risky. It held up for a specific reason. The cause was visible in every row: two extra operations per run, deterministic, no exceptions. When the added work is countable on each run, attribution does not depend on two time windows looking alike. For a change whose effect is statistical rather than mechanical, a canary against a concurrent control would be the stronger test.

The measurement also corrected the complaint in both directions. The reported 5.9 seconds matched the logs. The remembered 1–3 second baseline did not; the measured baseline was 2.7–3.5 seconds. That is the practical case for writing objectives down in advance, as the error budget policy does. A target recorded before the incident cannot drift with memory.

Finally, the fix matched the signal. Since only latency had moved, we kept the feature, moved the heavy step into a background job, and purged outdated file versions on every update. Our own counter-example still stands: had the logs shown more errors, or had the extra work been non-idempotent and corrupting data, rolling back would have been the safer move.

A checklist before you approve a rollback

1. Restate the complaint as a number: which operation, which path, how slow, compared with what.
2. Pull the baseline from logs. Do not accept a remembered baseline from either side.
3. Check latency and errors separately. Note timeouts and failed runs on their own line.
4. Count the work per run (operations, calls, writes) so you can tie the change to the effect.
5. If the effect is noisy, compare against a control running at the same time instead of an earlier window.
6. Match the response to the signal. Rising errors or data corruption point to rollback. Latency alone points to moving work off the user-facing path or optimizing it.
7. If you move work into the background, add retries, visibility and an alert for repeated job failures.
8. Write down, in advance, the number that would make you roll back. Then hold to it.

More questions

Is a before/after comparison of logs good enough evidence?

Sometimes. The SRE workbook calls before/after evaluation risky because other things change between the two windows. It worked in our case because the extra operations appeared in every single run, so the cause was directly attributable. If the effect you are chasing is a shift in averages with no countable mechanism, run the new version next to the old one and compare them at the same time.

Can added latency alone justify a rollback?

Yes, if latency is part of what you promised and the change pushes you past that objective. The SRE book treats latency as a first-class signal, and an error budget policy can be written around it. What the documentation does not support is rolling back on a feeling. Decide the threshold first, measure against it, and let the number make the call.