Almost five years ago I published at manifesto of sorts at https://principlesofchaos.org to define a new discipline in software engineering called Chaos Engineering. It wasn’t about creating chaos, but rather identifying the chaos inherent in a complex system. The other practices that commonly address availability (incident management, alerting, monitoring, disaster recovery, etc) are all reactive: they focus on time to detect, and time to remediate. Chaos Engineering on the other hand is proactive: finding systemic vulnerabilities before they affect customers. Now that Chaos Engineering has high adoption at big tech companies and non-digital native orgs alike, we can look at how the practice is maturing. Our knowledge of systemic properties of complex systems is improving and leading us into a new era of Continuous Verification.

Almost five years ago I published at manifesto of sorts at https://principlesofchaos.org to define a new discipline in software engineering called Chaos Engineering. It wasn’t about creating chaos, but rather identifying the chaos inherent in a complex system. The other practices that commonly address availability (incident management, alerting, monitoring, disaster recovery, etc) are all reactive: they focus on time to detect, and time to remediate. Chaos Engineering on the other hand is proactive: finding systemic vulnerabilities before they affect customers. Now that Chaos Engineering has high adoption at big tech companies and non-digital native orgs alike, we can look at how the practice is maturing. Our knowledge of systemic properties of complex systems is improving and leading us into a new era of Continuous Verification.

Almost five years ago I published at manifesto of sorts at https://principlesofchaos.org to define a new discipline in software engineering called Chaos Engineering. It wasn’t about creating chaos, but rather identifying the chaos inherent in a complex system. The other practices that commonly address availability (incident management, alerting, monitoring, disaster recovery, etc) are all reactive: they focus on time to detect, and time to remediate. Chaos Engineering on the other hand is proactive: finding systemic vulnerabilities before they affect customers. Now that Chaos Engineering has high adoption at big tech companies and non-digital native orgs alike, we can look at how the practice is maturing. Our knowledge of systemic properties of complex systems is improving and leading us into a new era of Continuous Verification.

Since Taylor's theories of scientific management in the early 20th century, most management has focused on improving performance by changing behavior, and changing behavior by changing motivation. Turns out, people are more creatures of habit than they are of motivation or calculation. Psychological safety, engagement, and caring about people are all important for the leader of an organization, including a technical organization. To take that a step further and get a high performance team, most engineering organizations need to be de-bureaucratized. Most attempts to improve engineering efficiency focus on process. That's difficult and boring. Fortunately, you can get better results by making actual engineering decisions that restructure organizational decision-making, turning normal teams into high performance teams.

Since Taylor's theories of scientific management in the early 20th century, most management has focused on improving performance by changing behavior, and changing behavior by changing motivation. Turns out, people are more creatures of habit than they are of motivation or calculation. Psychological safety, engagement, and caring about people are all important for the leader of an organization, including a technical organization. To take that a step further and get a high performance team, most engineering organizations need to be de-bureaucratized. Most attempts to improve engineering efficiency focus on process. That's difficult and boring. Fortunately, you can get better results by making actual engineering decisions that restructure organizational decision-making, turning normal teams into high performance teams.

When engineering teams take on a new project, they often optimize for performance, availability, or fault tolerance. More experienced teams can optimize for these properties simultaneously. Now add an additional property: feature velocity. Mental models of architecture can help you understand the tension between these engineering properties. For example, understanding the distinction between accidental complexity and essential complexity can help you decide whether to invest engineering effort into simplifying your stack or expanding the surface area of functional output. Chaos Engineering was born within this conflict between feature velocity and increasing complexity. Rather than simplify, Chaos Engineering provides a mechanism for you to embrace the complexity and ride it like a familiar wave, maintaining our business priorities while dialing up feature velocity.

When engineering teams take on a new project, they often optimize for performance, availability, or fault tolerance. More experienced teams can optimize for these properties simultaneously. Now add an additional property: feature velocity. Mental models of architecture can help you understand the tension between these engineering properties. For example, understanding the distinction between accidental complexity and essential complexity can help you decide whether to invest engineering effort into simplifying your stack or expanding the surface area of functional output. Chaos Engineering was born within this conflict between feature velocity and increasing complexity. Rather than simplify, Chaos Engineering provides a mechanism for you to embrace the complexity and ride it like a familiar wave, maintaining our business priorities while dialing up feature velocity.

When engineering teams take on a new project, they often optimize for performance, availability, or fault tolerance. More experienced teams can optimize for these properties simultaneously. Now add an additional property: feature velocity. Mental models of architecture can help you understand the tension between these engineering properties. For example, understanding the distinction between accidental complexity and essential complexity can help you decide whether to invest engineering effort into simplifying your stack or expanding the surface area of functional output. Chaos Engineering was born within this conflict between feature velocity and increasing complexity. Rather than simplify, Chaos Engineering provides a mechanism for you to embrace the complexity and ride it like a familiar wave, maintaining our business priorities while dialing up feature velocity.