How lack of follow through and visibility keeps you stuck on the outage treadmill
Every engineering leader has seen some version of this story:
You suffer a painful incident.
The team rallies, triages, mitigates.
A postmortem gets written. Action items get logged.
Everyone exhales, the roadmap reclaims attention, and life moves on.
Then a few weeks or months later, a strikingly similar incident hits. Different ticket, same pattern. Slack fills with familiar messages:
At this point, the default reaction is to treat it as an Operations issue.
In reality, recurring incidents are almost never just an Ops failure.
They are a learning failure.
You do not have a reliability problem as much as you have a memory, follow through, and visibility problem.
Most organizations are good at what we can call the short loop:
This loop is intense and visible. Customers are impacted. Executives are watching. Heroics are rewarded. People feel the urgency.
But there is a second loop that actually determines whether the same class of failure will return.
The long loop looks more like this:
This loop is slow, cross functional, and often invisible. It spans weeks or quarters. It competes with feature delivery. It is nobody's full time job.
Recurring incidents show up when:
When your long loop is broken, the same failure patterns quietly reappear, even though everyone feels like the team is "doing postmortems."
If you look honestly at your recurring incidents, the symptoms usually rhyme.
After an incident, teams create action items with good intentions:
Then reality hits.
These items end up in a backlog that already has 300 tickets. They compete with committed roadmap work. There is no clear prioritization framework that says:
So action items:
By the time a long running mitigation is needed, the original incident responders may have moved teams or roles. The incident channel is archived. The postmortem doc is somewhere in Confluence or Google Drive.
Nobody remembers:
Ownership dissolves into the ether and with it, momentum.
Incidents are emotionally expensive. People feel guilt, frustration, or embarrassment. It is natural to want closure as soon as the system is stable again.
That emotional desire for closure often translates into premature technical closure:
The organization gets short term relief at the cost of long term learning.
Even if your teams are motivated to learn, they often run into a second problem: they cannot actually see the system in a way that makes learning easy.
For a single incident, raw context lives across:
To understand a recurring pattern, you have to mentally stitch these artifacts across multiple incidents and many months.
Most teams do not do that. Not because they do not care, but because the friction is too high.
Even when you have dozens or hundreds of postmortems, they are usually stored as independent documents. Without a unifying lens, it is hard to answer questions like:
As a result, you optimize for individual incidents instead of reducing whole classes of failure.
During an incident, people fall back on what they remember. That memory is often shallow and local:
What they cannot see easily:
Without that context, responders are forced to rediscover known failure paths. That is not resilience, it is institutional amnesia.
If you step back, recurring incidents show you where your organizational memory is weak.
You might have:
The result is a familiar pattern:
That is a learning system failing, not an Ops team failing.
To break this cycle, you need a different model for incident learning. A strong long loop has a few key characteristics.
Instead of leaving insights trapped in chat logs and docs, you normalize them into a consistent, queryable format:
When that structure exists, your organization can start to see patterns across incidents, not just within each one.
Rather than closing everything once the immediate fix lands, you explicitly track long running threads:
These threads stay visible until you have shipped meaningful changes, validated by real incident data, not just intuition.
Roadmaps and architecture reviews should explicitly reference your incident learning:
When learning shows up in planning conversations, it starts to influence how you spend time and budget, not just how you write documents.
This is exactly the gap we built COEhub to close.
COEhub turns your incident history into a living memory system that supports both the short loop and the long loop.
COEhub connects to the tools you already use for incidents:
It pulls incident data, transcripts, and timelines together, then converts them into structured, consistent incident records.
Instead of trawling across five tools, you get a unified view that is ready for analysis, search, and trend detection.
COEhub helps you spot and track recurring themes:
You can track these as long lived resilience threads, not one off action items that quietly drift into backlogs.
The next time there is an outage, your responders can ask COEhub:
Instead of relying on tribal memory, you rely on an institutional one. Incidents stop being isolated events and start feeding into a shared understanding.
Because COEhub keeps a structured view of incidents, threads, and actions, it becomes easier to:
The long loop becomes visible, measurable, and manageable.
If recurring incidents are haunting your teams, it is tempting to double down on Ops:
These are necessary, but not sufficient.
Until you strengthen your learning system, your organization will keep paying for the same lesson over and over.
The shift is simple to describe, but hard to execute:
COEhub exists to make that shift practical. It gives your organization long term memory so you can finally close the loop on recurring incidents, reduce structural risk, and build systems that get more resilient with every outage, not less.
If you are tired of seeing the same incident movie play on repeat, it might be time to upgrade your learning system, not just your alerts.