Chaos engineering is the practice of experimenting on a system to build confidence in its ability to withstand turbulent conditions in production.
At first glance, this sounds counterintuitive.
"Why would you break something that’s working perfectly fine?" you might ask. It’s a question I’ve encountered many times, even from experienced engineers and managers.
The notion of purposefully introducing failure into a system can spark anxiety, scepticism, and even outright rejection.
I remember vividly when I first proposed chaos engineering to one of my teams. They looked at me as if I had just suggested we abandon the project altogether. “If it’s working, don’t touch it!” was the immediate response.
To be honest, I was a bit disappointed.
They didn’t understand what I was trying to convey. The point of chaos engineering isn’t to break systems for the sake of breaking them. It’s about building resilient systems that can withstand the inevitable failures and unpredictable conditions we face in production.
It’s about understanding our systems better, testing them in real-world scenarios, and ultimately making them stronger.
But it wasn’t until we started running a few drills that the team began to see the benefits. Fast forward a few years, and chaos engineering had become a core part of our strategy for improving reliability and stability.
However, when I attempted to reintroduce it in a different context with a new team, the resistance was immediate.
This time, the engineers pushed back hard, accusing me of putting their jobs at risk. It was then that I realized the issue wasn’t the concept of chaos engineering itself. The real problem was that we lacked proper documentation and shared knowledge.
Much of the technical know-how was siloed in the heads of a few key individuals—a phenomenon I call “technical sequestration.”
This realization drove me to reflect deeply on the dynamics of chaos engineering, its benefits, challenges, and, most importantly, how to successfully implement it without facing pushback.
In this article, I’ll walk you through my personal journey with chaos engineering, while providing a comprehensive understanding of why it’s a critical discipline for modern systems.
I’ll also discuss how you can overcome the inevitable resistance from teams and stakeholders who fear the impact of breaking things on purpose.
The Evolution of Chaos Engineering
Chaos engineering, in its current form, originated from Netflix in the early 2010s. As the company transitioned from a DVD rental service to a streaming platform, its infrastructure evolved into a highly distributed, cloud-based system.
With this shift came new challenges. Traditional forms of testing, like unit tests or integration tests, couldn’t account for the unpredictable behaviour inherent in distributed systems.
Netflix engineers needed a way to test how their system would behave under real-world, chaotic conditions.
Enter Chaos Monkey, the first chaos engineering tool designed to randomly terminate instances within their infrastructure to test resilience.
The success of Chaos Monkey laid the foundation for an entire suite of tools and methodologies, now collectively referred to as chaos engineering.
Today, chaos engineering is used by companies like Google, Amazon, Microsoft, and Uber to ensure their systems remain robust, even in the face of unexpected failures.
But despite its widespread adoption, chaos engineering remains a misunderstood and sometimes controversial practice, particularly in smaller organizations or teams where fear of disruption looms large.
Why Chaos Engineering Matters
At its core, chaos engineering is about learning how your system behaves in the face of failure. It’s not a question of if your system will fail but when.
By proactively injecting failures—whether that’s by shutting down services, introducing latency, or cutting off network access—you gain valuable insights into your system’s vulnerabilities. These insights help you improve your system’s design and build confidence in its ability to recover from real-world incidents.
Think of chaos engineering as a fire drill for your system. When a fire breaks out in a building, it’s not the time to figure out where the exits are or how the sprinkler system works. You want to know those things before disaster strikes.
The same principle applies to chaos engineering. You run controlled experiments in a safe environment so that when something does go wrong in production, you’re prepared.
That said, chaos engineering is about more than just resilience. It’s also about improving observability, fostering a culture of continuous learning, and encouraging teams to think critically about their system’s design.
The goal isn’t to break things for the sake of breaking them; it’s about building stronger, more reliable systems that can deliver consistent performance, even in the face of adversity.
Overcoming Resistance: “If It Ain’t Broke, Don’t Fix It”
Now, while all of this sounds great in theory, getting buy-in for chaos engineering is often easier said than done. As I mentioned earlier, when I first introduced the concept to my team, their initial reaction was one of disbelief.
"Why would we intentionally break something that’s working?" they asked.
It’s a fair question. After all, we’ve been conditioned to think that systems should be stable, and any intentional disruption seems like a step backwards.
But the truth is, stability is an illusion. In the complex, distributed systems we operate today, failures are inevitable. Whether it’s a server crashing, a network partition, or a third-party service going down, something will go wrong at some point.
The question isn’t whether your system will fail, but how it will fail and whether you’re prepared to handle it.
I quickly realized that the resistance I faced wasn’t just about chaos engineering. It was about fear—fear of breaking something, fear of disrupting production, fear of losing control. But chaos engineering isn’t about losing control.
It’s about gaining control by understanding how your system behaves under stress and learning from those insights.
Building a Culture of Chaos: A Step-by-Step Guide
If you’re looking to implement chaos engineering in your organization, it’s important to take a gradual approach. Start small, and scale up as your team becomes more comfortable with the process. Here’s a step-by-step guide to help you get started:
1. Start with a Small Blast Radius
One of the most important principles of chaos engineering is to minimize the blast radius. In other words, start small. Don’t begin by shutting down your entire production environment. Instead, focus on a single service or component, and introduce a minor failure.
For example, you might simulate a network delay or temporarily shut down a non-critical service.
By starting small, you minimize the risk of causing widespread disruption, while still gaining valuable insights into how your system responds to failure.
2. Create a Hypothesis
Before running any chaos experiment, you need to create a hypothesis. This is a statement that predicts how your system will behave in the face of failure.
For example, “If Service A goes down, the system will automatically failover to Service B with no downtime.”
The key here is to test your assumptions.
You may think you know how your system will behave, but chaos engineering often reveals unexpected behaviour that you wouldn’t have otherwise discovered.
3. Run Controlled Experiments
Once you’ve created your hypothesis, it’s time to run the experiment. This should be done in a controlled environment with clear stop conditions in place. For example, if your experiment starts to cause unexpected disruptions, you should have a way to quickly revert the changes and restore normal operation.
Remember, the goal is not to cause chaos for the sake of chaos. It’s about learning how your system behaves under stress and using that knowledge to improve its resilience.
4. Analyze the Results
After the experiment is complete, take the time to analyze the results. Did the system behave as expected? Were there any surprises? What can you learn from the experiment, and how can you use that knowledge to improve your system’s design?
This is where chaos engineering really shines. By identifying and addressing weaknesses in your system, you can prevent future outages and improve overall reliability.
5. Gradually Increase the Complexity
As you become more comfortable with chaos engineering, you can start to increase the complexity of your experiments. For example, you might simulate multiple failures at once or introduce more severe disruptions, such as cutting off access to an entire data center.
The key is to gradually scale up the experiments, always keeping the blast radius in check. By doing so, you can build confidence in your system’s ability to handle increasingly chaotic conditions.
Technical Sequestration: The Silent Saboteur
While chaos engineering is a powerful tool for improving system resilience, it can also expose underlying organizational issues—one of which is what I call “technical sequestration.”
This occurs when critical knowledge and expertise are concentrated in the heads of a few key individuals, rather than being documented and shared across the team.
In one of my more recent attempts to introduce chaos engineering, I faced immediate resistance from the engineering team.
They were worried that if we started running chaos experiments, they would be blamed for any failures and potentially lose their jobs.
I realized this fear’s root was the lack of proper documentation and shared knowledge. If something went wrong, there was no clear playbook for how to fix it. All the know-how was locked away in the heads of a few senior engineers.
This is a dangerous situation for any organization.
When critical knowledge is concentrated in a few individuals, it creates a single point of failure. If those individuals leave the company, or if they’re unavailable during a crisis, the entire team is left scrambling to figure out how to resolve the issue.
To overcome this, it’s essential to prioritize documentation and knowledge sharing.
Create clear runbooks, document failure scenarios, and ensure that everyone on the team has a basic understanding of how the system works. By doing so, you can reduce the risk of technical sequestration and build a more resilient, self-sufficient team.
The Fear Of Applying Chaos Engineering
There’s an unspoken rule in IT: if you break things, you’re penalized. Mistakes happen more often than you’d think, usually due to not fully understanding what the system is doing in the background or because of hidden dependencies.
That’s why thorough documentation is crucial. However, in my twenty years in IT, I’ve noticed that the fear of breaking things often stems from trying to conceal poor implementations, which lead to critical incidents popping up here and there. These incidents are usually met with indifference and the typical “open a ticket and close it immediately,” followed by endless discussions about whether the incident should be classified as P1 (Very Critical) or P2 (Critical), and those moments when someone decides to downplay it as a P3 (Non-Critical) after the business has been down for over an hour—all in the name of “cooking the books” on SLAs.
Breaking things isn’t about acting carelessly. Chaos itself has a purpose. In chaos engineering, we intentionally trigger certain disruptions to see how the platform behaves and whether our assumptions hold up. It’s like buying a motorcycle that claims it can go 220 km/h and testing it to see if it can sustain that speed—and even what happens if you push it further (assuming you’re doing this in a controlled environment).
I firmly believe that a manager or someone with accountability should have a button to disrupt the platform to verify the claims of engineers who are confident that it can handle anything.
Chaos engineering should be part of any test system; that’s why we have car tests on plains, etc. But in IT, certain engineers prefer to follow the tutorial and leave it as it is, only to come back to it whenever a patch must be applied or an incident occurs.
The Role of Automation in Chaos Engineering
One key aspect of successful chaos engineering is automation. Manually running chaos experiments can be time-consuming and prone to human error. By automating these processes, you ensure that experiments are run consistently and at scale, reducing the risk of introducing unintentional variables that could skew the results.
Automated chaos experiments also allow you to integrate chaos engineering into your continuous delivery pipeline.
This means that whenever you deploy new code or make changes to your system, chaos experiments can be triggered automatically to test the system’s resilience. Automation ensures that resilience testing becomes a routine part of your development process, rather than an ad-hoc activity.
There are several tools available that help automate chaos experiments.
Tools like Gremlin, Chaos Monkey, LitmusChaos, and AWS Fault Injection Simulator (FIS) allow you to introduce failures in a controlled manner, monitor their impact, and automate recovery processes.
These tools are designed to help teams focus on improving system resilience without having to worry about manually managing the chaos experiments themselves.
Documentation as a Foundation for Chaos Engineering Success
As I mentioned earlier, one of the main challenges I faced when introducing chaos engineering was the lack of documentation. In many organizations, key technical knowledge is often stored in the minds of a few experts, creating a situation I refer to as “technical sequestration.”
This is a significant barrier to chaos engineering because, without proper documentation, the team lacks a clear roadmap for handling failures.
Good documentation is essential for chaos engineering to succeed. It ensures that everyone on the team understands the system architecture, responds to different failure scenarios, and collaborates effectively during chaos experiments.
When engineers know that there’s a detailed playbook in place, they’re more likely to embrace chaos engineering because it reduces the uncertainty and fear that come with breaking things on purpose.
Documentation should cover everything from system dependencies and failure recovery procedures to postmortem analysis and lessons learned from past chaos experiments.
It’s important to ensure that this knowledge is shared and accessible to everyone, not just a select few.
Regular updates to documentation, as systems evolve, are also critical to keeping chaos engineering efforts relevant and effective.
Overcoming Fear and Resistance: A Cultural Shift
One of the biggest hurdles to implementing chaos engineering is overcoming engineers’ and management’s fear and resistance.
Many teams, especially those unfamiliar with the discipline, worry that intentionally breaking systems will lead to outages, loss of productivity, and, in the worst-case scenario, job loss. This fear is understandable but ultimately unfounded when chaos engineering is done right.
To address these concerns, it’s crucial to communicate the benefits of chaos engineering clearly. Focus on the idea that it’s not about breaking things recklessly but rather about controlled experimentation aimed at improving system reliability.
Use analogies that people can relate to, like comparing chaos experiments to fire drills or safety inspections, which are designed to prepare for potential disasters.
It’s important to emphasize that chaos engineering is not about assigning blame when things go wrong. It’s a learning tool that helps teams identify weaknesses and improve processes.
By fostering a culture of psychological safety—where engineers feel comfortable experimenting and learning from failures—you can help reduce the fear and resistance that often come with chaos engineering initiatives.
Involving management early in the process is also key to securing buy-in. Present chaos engineering as a strategic investment in system reliability that can prevent costly outages and improve overall performance.
Show them how companies like Netflix, Google, and Amazon have successfully implemented chaos engineering to reduce downtime and enhance user experience.
Highlighting the business value of chaos engineering will make it easier to get leadership on board.
The Long-Term Benefits of Chaos Engineering
Chaos engineering offers numerous long-term benefits that extend beyond simply improving system reliability.
Here are some of the key advantages that organizations can expect to gain from adopting this discipline:
1. Increased System Resilience
By proactively identifying and addressing potential failure points, chaos engineering helps build more resilient systems that can handle unexpected disruptions. This resilience translates to reduced downtime, faster recovery times, and ultimately, better customer experiences.
2. Improved Team Collaboration
Chaos experiments often involve cross-functional teams working together to diagnose and resolve issues. This fosters a culture of collaboration and shared responsibility, where teams are better equipped to respond to incidents and improve the overall health of the system.
3. Faster Incident Response
When teams regularly practice chaos engineering, they become more adept at identifying the root cause of failures and responding to incidents. This leads to faster incident resolution times and reduces the impact of outages on end users.
4. Continuous Learning and Improvement
Chaos engineering promotes a mindset of continuous learning. Every experiment provides new insights into how the system behaves under stress, which can be used to refine processes, improve system design, and prevent future failures.
5. Reduced Mean Time to Recovery (MTTR)
Chaos experiments simulate real-world failures, allowing teams to test their incident response procedures. By practising recovery processes regularly, teams can reduce their mean time to recovery (MTTR) when actual failures occur, minimizing the impact on customers and the business.
Practical Chaos Engineering: Implementing in Real-World Systems
Implementing chaos engineering in the real world requires a combination of technical expertise, cultural change, and careful planning. Below are some practical tips to help guide the implementation process:
1. Run Chaos Experiments in Staging First
Before running chaos experiments in production, it’s a good idea to start in a staging environment. This allows you to test your assumptions, refine your experiments, and ensure that your system can handle failures without affecting real users.
2. Focus on Critical Systems
Start by running chaos experiments on the most critical parts of your infrastructure. These are the systems that have the highest impact on your business if they fail. By focusing on the areas that matter most, you can maximize the value of your chaos engineering efforts.
3. Automate Where Possible
As mentioned earlier, automation is key to scaling chaos engineering. Use tools like Gremlin or Chaos Monkey to automate chaos experiments and integrate them into your continuous delivery pipeline. This allows you to run experiments consistently and at scale, without needing manual intervention.
4. Conduct Postmortems for Every Experiment
After each chaos experiment, conduct a postmortem to analyze the results. Identify what went well, what didn’t, and what can be improved. Use this information to refine your chaos engineering strategy and make informed decisions about future experiments.
5. Share Findings with the Team
Transparency is crucial when it comes to chaos engineering. Make sure to share the results of your experiments with the entire team. This helps everyone learn from the experience and fosters a culture of continuous improvement.
Conclusion: Embracing the Chaos
Chaos engineering may seem counterintuitive at first glance, but it’s an essential discipline for building resilient, reliable systems in today’s complex, distributed environments. By proactively testing your system’s ability to withstand failure, you can identify vulnerabilities, improve incident response times, and ultimately deliver a better experience for your users.
However, chaos engineering isn’t just about the technology. It’s about fostering a culture of experimentation, learning from failures, and working together as a team to improve system resilience.
While there may be initial resistance—whether from engineers worried about job security or managers concerned about disruption—the long-term benefits far outweigh the risks.
By starting small, automating experiments, documenting processes, and sharing knowledge across the team, you can overcome the fear and uncertainty that often accompany chaos engineering initiatives. In doing so, you’ll build stronger, more reliable systems that can withstand even the most chaotic of conditions.
So, the next time someone asks, “Why would you break something that’s working?” you can confidently reply, “Because it’s the only way to make sure it won’t break when we need it most.“
Key Takeaways:
- Overcoming resistance requires clear communication, a focus on psychological safety, and demonstrating the long-term value of chaos engineering.
- Chaos engineering is about proactively testing systems to identify vulnerabilities before they cause real-world issues.
- Start small with a limited blast radius and gradually increase the complexity of your experiments.
- Automation is key to scaling chaos engineering and integrating it into your continuous delivery pipeline.
- Proper documentation and knowledge sharing are essential to overcoming technical sequestration and ensuring chaos engineering success.