When AWS suffered a major regional outage and Azure followed weeks later with disruptions of their own, the news cycle focused on the familiar metrics: minutes of downtime, SLA violation calculations, which Fortune 500 companies were affected, and how quickly the providers restored service.
That framing misses the more important question. These were not anomalies. They were predictable outcomes of conditions that had been building for years inside both organizations. And those same conditions exist, in some form, inside most technology organizations operating at scale.
The outages are worth examining not because of what they reveal about hyperscale cloud providers, but because of what they reveal about the patterns that precede technical failures, patterns that show up in companies with 15 engineers as reliably as they show up in companies with 15,000.
What Actually Happened
Both outages started with configuration changes. In both cases, the changes themselves were not categorically different from thousands of other changes deployed without incident. What made these different was what happened after the change was applied: automated systems picked up the new state, misread it, and propagated a cascading failure across multiple availability zones before any human had processed what was happening.
By the time alerts reached on-call engineers, the failure scope was already set. The restoration work was real and technically demanding. But the blast radius, the duration, and the customer impact were largely determined by decisions that had been made months and years earlier, not in the hours the incidents were active.
The Three Conditions That Precede These Failures
In working with companies across a range of industries and technical maturity levels, I have seen three conditions that consistently precede incidents of this type. All three were present in both the AWS and Azure situations.
1. The compression of the human review layer
Both organizations had, over the preceding years, reduced the number of engineers with direct production authority in favor of automated deployment pipelines. The rationale was straightforward: automation is faster, more consistent, less prone to individual error, and scales without adding headcount.
That reasoning is correct as far as it goes. The tradeoff it does not account for is that automation executes without judgment. A pipeline does not know that the configuration change it is deploying looks correct syntactically but is wrong contextually. It does not have the experience to recognize a change that has been deployed in lower environments without incident but will behave differently under production load. It deploys what it is told to deploy.
When the human review layer is compressed and the approval process becomes a checkbox rather than a judgment call, the automation stops being an extension of engineering capacity and becomes a replacement for it. The speed advantage is real. The risk is that when something is wrong, the pipeline amplifies the error at the same speed it amplifies correct changes.
2. Knowledge concentration in undocumented systems
Both outages required root cause analysis under time pressure. That analysis depends on engineers who understand not just what the system does, but why it was built the way it was: what constraints shaped the original design, what tradeoffs were made, and what assumptions are embedded in the architecture that are not visible in the code itself.
In both cases, that knowledge was distributed unevenly. It was not that the organizations were careless about documentation. It was that the systems had evolved over years of sprints, acquisitions, and team transitions. The engineers who made the original design decisions had moved to other teams or left the company. The engineers who maintained the systems had deep operational knowledge but less context on the reasoning behind the decisions they were maintaining.
This is not a documentation problem. Documentation captures what a system does. It rarely captures why a system is the way it is. That knowledge lives in the people who were in the room when the decision was made. When those people are not available during an incident, the team is working from an incomplete picture.
3. The absence of practiced failure scenarios
Both organizations had incident response protocols. Both had runbooks documenting what to do when specific failure modes occurred. What neither had done recently enough was actually run a realistic multi-region failure scenario under time pressure with degraded tooling. Those were the conditions that actually existed during the incidents.
There is a meaningful difference between knowing what the runbook says and having recently executed it under conditions that resemble the real thing. The former is memory. The latter is muscle memory. When an incident arrives, the team that has practiced the failure makes better decisions faster than the team that has only read about it.
Game days and tabletop exercises are valuable. They are not substitutes for running the actual failure scenario in a controlled environment with the actual team that would respond to a real incident.
Why This Pattern Repeats
These three conditions (compressed human review, knowledge concentration, and unpracticed failure response) are not the result of organizational negligence. They are the result of success.
Organizations that have been operating well for an extended period gradually reduce the friction they associate with slowness without always recognizing that some of that friction was doing protective work. Approval gates feel bureaucratic when nothing has gone wrong recently. Knowledge transfer feels less urgent when the person who holds the knowledge is still there. Failure scenario practice feels low-priority when the last serious incident was three years ago.
The conditions build quietly. They do not announce themselves. They become visible when the incident arrives that the organization was not quite prepared for.
What to Look for in Your Own Organization
The diagnostic is not complicated. It requires honesty about conditions that are easy to rationalize.
On the human review layer: when your team deploys a change to production, is there a human being who has reviewed it for contextual correctness, not just technical validity, and who owns the decision? Or has that step become an automated gate that checks syntax and passes tests? If it is the latter, you have compressed the judgment out of the deployment process.
On knowledge concentration: who in your organization understands the reasoning behind your most critical architectural decisions, not just what the architecture is? If the answer is one or two people, ask when the last time was that knowledge was actively transferred to someone else. Then ask what happens to your incident response capability if those people are unavailable.
On failure scenario practice: when did your team last run a realistic failure scenario, not a tabletop discussion, but an actual exercise with the real response team under conditions that resemble what a serious incident looks like? If the answer is more than twelve months ago, the runbook knowledge your team has is theoretical.
The Gap That Matters Most
In every organization I work with, the technical team has a reasonably clear picture of where the risk is. The gap is not usually in the identification. It is in the translation of that risk picture into a form that reaches the people making resourcing and prioritization decisions.
The AWS and Azure outages were not surprises to the engineering teams closest to the systems. The conditions that preceded them were visible to people who knew where to look. The organizational decisions that allowed those conditions to persist were made by people who were not looking at the same picture.
That gap, between what the engineering team knows and what the executive layer is deciding from, is where most technical risk actually lives. Closing that gap is not a technical problem. It is a leadership problem.
The outages will be forgotten faster than the patterns that caused them. The patterns will repeat.
Michael Snyder is a Fractional CTO working with founders and CEOs at growth-stage companies when technology decisions carry consequence. Based in Austin, TX.
If the three conditions described in this article sound familiar, the Structural Clarity Diagnostic is a structured way to surface them.