There’s a certain chill in the air, and it’s not just the holiday weather. For many tech teams, the signs of impending failure are starting to surface. Frequent incidents, slow root cause analysis (RCA), and fear of Friday deployments are all clear indications that something’s not right.
On 2 Days Until Christmas of our 10 Days of Christmas Chaos, we’re talking about the red flags that signal it’s time for your organization to embrace Chaos Engineering. If you’ve ever had an outage spiral out of control, or if you’ve delayed a deployment “just to be safe,” this article is for you.
Just like Santa’s list, we’re checking it twice — and if any of these signs show up, it’s time to start introducing controlled chaos.
🎁 Red Flag #1: Frequent Incidents and Slow Root Cause Analysis (RCA)
The Sign: Your team is constantly responding to incidents, and every post-incident review ends with, “We’re not exactly sure what happened.”
Why It’s a Problem: Frequent incidents can be a sign that your systems aren’t resilient to failure. Even worse, if your team struggles with Root Cause Analysis (RCA), it’s a sign that you don’t fully understand how your system behaves under stress.
How Chaos Engineering Can Help:
- Run chaos experiments to expose failure modes before incidents happen.
- Use chaos to identify which parts of your system are “black boxes” where you’re unsure how failures propagate.
- Use post-experiment reviews to improve RCA workflows and build a “chaos playbook” for future incidents.
🎉 Pro Tip: Treat every chaos experiment as if it’s a live incident. Track response times, communication issues, and escalation paths to see where your RCA process breaks down.
Real-World Example: A major e-commerce company adopted Chaos Engineering after experiencing a 6-hour outage on Black Friday. Their RCA revealed that they had “assumed” traffic would auto-balance between availability zones. Post-chaos experiments revealed the failover process was never properly tested.
🎁 Red Flag #2: Teams Are Afraid to Deploy on Fridays
The Sign: Deployments are mysteriously delayed until Monday, and your team’s mantra is, “Don’t ship on Fridays.”
Why It’s a Problem: Fear of change is a major sign that your team doesn’t trust the system’s resilience. This “fear-driven development” stifles agility and slows down feature delivery.
How Chaos Engineering Can Help:
- Introduce “chaos in CI/CD” pipelines by running resilience checks on every release.
- Schedule pre-deployment chaos experiments to see how new changes affect production stability.
- Build confidence in failover processes so teams feel safe deploying before the weekend.
🎉 Pro Tip: Use chaos experiments to test rollback mechanisms. If your rollback process is slow or broken, no one will want to deploy before a weekend.
Real-World Example: An entertainment streaming service introduced “release chaos” into its CI/CD pipelines. After 3 months, engineers reported feeling more confident deploying on Fridays because they knew each build had already passed chaos tests.
🎁 Red Flag #3: Single Points of Failure Still Exist
The Sign: There’s that one service, one API, or one database that, if it goes down, takes everything with it.
Why It’s a Problem: Single points of failure (SPOFs) are the ultimate system bottlenecks. If one service can bring down your entire system, you’re one step away from a major outage.
How Chaos Engineering Can Help:
- Run experiments that kill your “critical dependencies” (e.g., kill one of your read replicas) and observe what happens.
- Identify “hard” dependencies vs. “soft” dependencies and reduce reliance on hard dependencies.
- Use network partition experiments to test multi-region failover logic.
🎉 Pro Tip: If you’re not sure which services are your “single points of failure,” chaos experiments will reveal them fast.
Real-World Example: An online payments company discovered its “fraud detection API” was a single point of failure. After running chaos experiments, they built an emergency “bypass” route to continue processing payments even if the fraud API went down.
🎁 Red Flag #4: Long MTTR (Mean Time to Recovery) Metrics
The Sign: It takes hours to recover from incidents, and stakeholders demand to know, “Why does it take so long?”
Why It’s a Problem: Long MTTR (Mean Time to Recovery) means your team is slow to respond, identify, and resolve incidents. It’s often a sign that your response processes are inefficient or that systems lack self-healing capabilities.
How Chaos Engineering Can Help:
- Use chaos to simulate production failures and measure how long it takes to recover.
- Automate chaos experiments that test failover logic. (e.g., force a region failover and track how fast the system recovers.)
- Test “on-call readiness” by running surprise Game Day chaos experiments.
🎉 Pro Tip: Use chaos to practice incident drills with on-call teams, tracking how quickly they identify and resolve failures.
Real-World Example: An insurance tech company reduced its MTTR from 45 minutes to 15 minutes after running on-call drills where engineers practiced responding to network partitions. The chaos drills revealed flaws in their alerting and escalations, which they fixed.
🎉 Share Your Own “Signs of Chaos”
Have you ever worked on a team that didn’t want to deploy on Fridays? Or maybe you’ve faced the dreaded “we don’t know the root cause” post-incident meeting?
What’s the biggest sign you’ve seen that it’s time to adopt Chaos Engineering?
Drop your story in the comments, and the best ones will be featured in an upcoming post!
Here are a few to get you started:
- “We’re afraid to deploy on Fridays”
- “The whole system went down because one API failed”
- “We didn’t know if failover would work until it failed”
Submit your story and show the world that chaos isn’t just for Christmas — it’s a daily challenge we all face.
Closing Thoughts
If you’ve seen one of these red flags in your team, it’s time to consider Chaos Engineering. Fear of change, frequent incidents, long recovery times — these are all signs that your system isn’t as resilient as you think.
But the good news is that Chaos Engineering doesn’t have to be “chaotic.” It’s about controlled, scientific experimentation to identify weaknesses before they impact your users.
So this holiday season, don’t just watch for falling snowflakes — watch for signs of system failure too. Identify your biggest risk areas, run an experiment, and take one step closer to a more resilient future.🎄🎉
