Reliability Has Outgrown the Systems Supporting It
Reliability feels harder because expectations changed faster than systems did. This post explains why slowdowns persist and why it’s not a people problem.
Service reliability has outgrown uptime checks and component-level tools, creating friction that slows response, increases toil, and wears teams down.
Reliability feels harder today because people expect service to work smoothly from start to finish. But the underlying systems are still split into separate pieces that don’t naturally work together.
Most incidents don’t fail in one place; they happen across multiple systems that depend on each other, which means teams have to piece together data manually and guess at what’s really going on.
New tools, including AI, can’t close the gap when data is fragmented and service context is missing.
Start addressing reliability as a systems problem by building a shared service context that reduces manual work and speeds confident decisions.
Uptime checks can pass, high availability can be in place, and users still can’t complete basic actions. Pages load slowly, latency spikes, and requests stall — all without a single system flagged as down.
Availability measures whether a service is running. Reliability measures whether it’s working for the person using it — a meaningfully different standard.
Today, reliability is measured through speed and responsiveness in real use. When performance slows, the experience degrades even if systems are reachable. The Catchpoint SRE Report underscores this clearly: most site reliability engineering (SRE) teams treat slow performance as seriously as downtime, and management generally agrees.
How the Scope of Reliability Quietly Expanded
Service reliability today stretches across on-prem systems that continue to run critical workloads, cloud environments that scale dynamically and introduce frequent change, edge locations closer to users and traffic sources, third-party services embedded directly into application flows, and the public internet that connects all of these dependencies.
As that scope widened, responsibility widened with it. SRE teams are now expected to account for end-to-end service behavior, including issues that originate in dependencies they don’t directly control.
The Catchpoint SRE Report makes this shift visible through its survey data. Speed and responsiveness are repeatedly identified as factors that determine whether a service is trusted. Reliability is discussed less as a tooling concern and more as a broader responsibility tied to outcomes — less about whether systems are up and more about whether they’re delivering.
Most SRE respondents say performance degradation is as disruptive as downtime, and management largely agrees. At the same time, the report shows many environments still rely on basic uptime signals and siloed reliability metrics, which don’t fully capture slow behavior or user impact.
The findings show that reliability expectations expanded faster than the tooling and operating models built to support them.
Where Service Reliability Breaks Down: Between Systems, Not Within Them
Reliability problems tend to appear between systems, not inside a single component. In many cases, every component looks fine on its own (the service is up, checks are passing, nothing is throwing obvious errors), but users still see slow responses or failed actions.
“When a digital experience slows, users don’t care if it’s an outage or a delay. The result feels the same.”
Catchpoint
SRE Report 2026
Service Issues Rarely Originate in a Single Component
Modern services depend on many systems working together across software and infrastructure layers. A database may be healthy, an API may respond, and infrastructure metrics may look normal. The issue shows up when these pieces interact. Small delays add up, retries stack, failover paths trigger, redundancy kicks in, and the service slows down. One system alone doesn’t explain what users are experiencing.
Most Teams Rely on Dashboards and Synthetic Tests
To catch performance problems, most environments still rely on dashboards and alerts. The Catchpoint SRE Report shows that roughly two-thirds of organizations depend on dashboards and alerting, and more than half use synthetic monitoring. These tools help, but they look at different components of the system, not the service as a whole. Service-level constructs like service level objectives (SLOs) and service level agreements (SLAs) are used far less consistently.
SREs Have to Manually Connect Signals Across Tools
When issues span systems, SREs usually have to piece things together themselves. Logs are in one tool, metrics in another, and synthetic results in a third. During incidents, understanding what’s happening in real-time means manually connecting this information across tools.
It’s what happens when systems are built to observe components, not services.
Why Fragmentation Slows Decisions When Time Matters Most
During on-call incidents, visibility is often split across tools. One view shows infrastructure health. Another shows application behavior. A third shows synthetic results. When these don’t align, engineers stall, spending time on reconciliation work instead of the incident itself.
That time goes toward:
Lining up timelines across tools
Reconciling conflicting indicators
Confirming which issue to address before troubleshooting begins
A lot of that stall comes from missing shared context. The Catchpoint SRE Report shows that only about one in four organizations consistently evaluate whether performance improvements affect business metrics like revenue or NPS. During an incident, engineers can see something is wrong but rarely know how it’s affecting those metrics in real time.
That gap shows up more broadly. Reliability is still rarely treated as a true business-level KPI. Without that shared view, decisions around service delivery take longer.
The Human Cost of Reliability Outgrowing Its Systems
As reliability expectations expanded, the work supporting them became heavier in less visible ways. The challenge isn’t a lack of effort or discipline. It’s the ongoing drag created by systems that require people to fill in the gaps manually.
That drag shows up in day-to-day work:
Constant context switching between tools: Logs, metrics, dashboards, alerts, monitoring tools, and synthetic results are often located in different places. Moving between them is part of normal incident response, but it fragments attention and slows reasoning.
Repeating the same manual correlation work: Many incidents follow familiar patterns, yet each one requires rebuilding context from scratch. Timelines have to be aligned again. Signals have to be reconciled again. Over months, the same manual effort becomes a baseline assumption rather than a problem to solve.
Rising toil with little room to recover: The SRE Report shows a median reported toil of about 34% of an engineer’s time, roughly 13 hours a week for someone on a standard 40-hour schedule. At the same time, very few engineers report having protected learning time during work hours, which limits opportunities to reduce that toil.
Fatigue and quiet loss of confidence: The Catchpoint SRE Report notes that alert fatigue and integration gaps are common across organizations. When it takes longer to build a clear picture during incidents, hesitation increases. Over time, that friction shows up as exhaustion and reduced confidence, even among experienced engineers.
That fatigue is structural. It’s accumulated system drag, built up over years as reliability outgrew the systems designed to support it.
Why New Tools (Including AI) Haven’t Closed the Gap
New tools, including AI, were expected to ease reliability work. In some cases, they do help. But they haven’t closed the gap created by fragmented tools because these tools are being added on top of the same disconnected systems.
AI Shows Mixed Results in Reducing Toil
The Catchpoint SRE Report shows a split experience with AI. About half of the respondents say AI reduced their toil. Roughly a third report no change, and a smaller group say toil actually increased. AI and automation can remove some repetitive tasks, but it doesn’t consistently reduce the manual work needed to understand incidents.
Leaders See More Benefit Than Practitioners
The report also shows a clear difference in perspective. Leaders are more likely than practitioners to say AI has helped reduce toil. Engineers closer to incidents often see the added setup, tuning, and follow-up work that comes with new tools. From their side, AI support can feel uneven rather than transformative.
Fragmented Data Limits What AI Can Do
AI struggles when data is fragmented and lacks service-level context. When logs, metrics, and events are in different tools, AI has to reconcile gaps before it can be useful. The report shows low confidence levels around monitoring AI and ML reliability, and without clear relationships between systems, AI can detect issues but not explain behavior.
Recommendations Aren’t Trusted Without Context
When AI suggests an action during an incident, trust depends on understanding why. If the relationships between systems aren’t visible, recommendations feel risky. Engineers hesitate—not because they doubt AI, but because the system doesn’t show enough context to act confidently.
AI monitoring isn’t failing. AI tools are bounded by the systems they sit on top of. When data is fragmented, they detect problems they can’t explain.
Service Reliability Is Now a Systems Problem
The job itself changed. Expectations have expanded beyond keeping systems online to include how services behave for users across environments that are more distributed and more connected than before.
What hasn’t kept pace are the systems supporting that work. Many reliability challenges today come from filling in gaps manually, aligning context across tools, and rebuilding understanding during every incident. Without unified observability, that friction makes even familiar problems take longer and feel challenging to resolve.
Service reliability can’t be scaled through effort or heroics alone. Working harder helps in the moment, but it doesn’t remove the drag created by fragmented systems. Over time, that approach wears people down without fixing the underlying issue.
The next phase of reliability depends on systems designed for modern responsibility—systems that support end-to-end understanding, share context by default, and reduce the need for manual interpretation during incidents.
Engineers are now responsible for end-to-end service behavior, but much of the tooling and operating models still reflect a narrower, component-level view. Over time, that mismatch shows up as extra effort, slower decisions, and work that carries more weight than it should.
Turn service reliability from fragmented visibility into shared understanding
LogicMonitor helps teams see service behavior end-to-end, reduce manual correlation, and respond with confidence as systems get more complex.
