Rollbacks That Don’t Guess: A Practical Playbook for Microservices

When something goes wrong in production, the most common question an SRE asks is:

> “What changed?”

And the most common fix?

> “Roll it back.”

Rolling back to a previous version usually works like a charm—except when it doesn’t. Sometimes the previous version isn’t actually stable, or it’s hard to know which version is the right one to go back to. In a microservices setup, this problem gets worse: each service is built by different teams, deployed at different times, and can fail in very different ways.

So how can SREs be better prepared to roll back quickly and safely?

Let’s explore a practical approach we’ve been using—and how a simple dashboard (built with AI in minutes) can make rollback decisions boring again.

The Problem: Microservices + Rollbacks = Messy

In Kubernetes environments, developers usually build code, package it as Docker images, and push them through dev → QA → production. Most of the time this works fine.

But when production faces complex inputs and real-world traffic, issues can pop up that were never seen in QA. Suddenly, the SRE team is staring at dashboards, wondering which service broke and which version to roll back to.

A Rollback Dashboard for SREs

Imagine if every service had a single row in a dashboard with columns like:

• Service Name – which microservice we’re looking at.‍
• Current Image Tag – what’s running in production right now.
• Error Count – how many 4xx, 5xx, or error logs the service is producing in the last N minutes.
• Rollback Candidate – the safest image tag to roll back to.
• Change Log – commits from Git between the current and previous version.
• Who Changed It – the developer or team behind the change. (suspect)
• Risk Score – a quick measure of how risky the current image is (e.g., using latest tag, big Git diffs, short-lived images, missing scans, etc.).

Wouldn’t it make an SRE’s life simpler if they could just glance at this, see what changed, and click to roll back?

Making Rollbacks Boring (With AI)

Here’s the fun part: building this kind of dashboard doesn’t need months of tooling. With an AI agent, you can assemble it in minutes.

At DagKnows, we implemented exactly this:

• We chat with the AI agent.
• Ask it to pull service names, error logs, Git diffs, image history, etc.
• The agent fetches the data, organizes it into a table, and even adds risk scoring.
  

The result? An SRE can stop guessing and just land on evidence. And yes, this has made SRE life so boring that they can now spend time on more interesting things—like building cool games or writing blogs.

Example: Building the Dashboard

Notice how easy it becomes to make a call:

• For req-router, rollback is optional; the image has been stable and error counts are low.
• For cmd-exec, the risks are clear (using latest, no scans, big diff). We can confidently roll back to v4.4.

How to Build This with AI‍

Here’s how it works step by step:

1. Start with a basic table. Ask the AI agent for a list of services and their current image tags in prod.

2. Add error counts. Ask for the number of errors in the last 5 minutes.‍
3. Add ELK Error Links. Ask for the running ELK queries to identify errors, warnings and 4xx or 5xx status codes.
4. Add Git change logs. Ask it to show what commits were made since the last stable version.

5. Add rollback targets + risk scores. The agent applies simple policies (e.g., pick images that lived ≥ 7 days, flag if using latest).

In minutes, you have a custom rollback dashboard tailored to your environment. And you can keep refining it—adding new columns, adjusting thresholds, or swapping out data sources.

Diagram: Signals → Scorer → Rollback Recommendation

This is deliberately boring. In incident response, boring is good.

Key Takeaways

• Rollbacks in microservices are not trivial—but they don’t have to be scary.
• A few simple signals (current image, error logs, Git diffs, known-good version, risk flags) are enough to guide safe rollbacks.
• With AI, you can assemble a rollback dashboard on the fly—no need for heavyweight tooling.
• The outcome: rollbacks become boring, safe, and fast.

Conclusion

The next time you face a 2 a.m. incident, you don’t want to be guessing. You want evidence, a safe default rollback version, and a boring click.

We’ve made this part of our process at DagKnows, but the approach is generic enough for any SaaS SRE team. Start small, build your dashboard, and let AI do the tedious correlation.

And then—go do something fun. Because boring SREs are the best SREs.