In 2025, I resolve to eliminate escalations and finger pointing

Originally posted to causely.ai by Steffen Geissinger

Make escalations less about blame and more about progress

Microservices architectures introduce complex, dynamic dependencies between loosely coupled components. In turn, these dependencies lead to complex, hard to predict interactions. In these environments, any resource bottleneck, or any service bottleneck or malfunction, will cascade and affect multiple services, crossing team boundaries. As a result, the response often spirals into a chaotic mix of war rooms, heated Slack threads, and finger-pointing. The problem isn’t just technical—it’s structural. Without a clear understanding of dependencies and ownership, every team spends more time defending their work than solving the issue. It’s a waste of effort that undermines collaboration and prolongs downtime.

Yesterday, we resolved to spend less time troubleshooting in 2025.

Troubleshooting and escalation are closely intertwined. A single unresolved bottleneck can ripple outward, forcing multiple teams into reactive mode as they struggle to isolate the true root cause. This dynamic creates inefficiencies and delays, with teams often focusing on band-aiding symptoms instead of remediating and solving the root causes. To eliminate this friction, we need systems that do more than detect anomalies—they must provide a seamless view of dependencies, understand and analyze the performance behaviors of the microservices, assign ownership intelligently, and guide engineers toward resolution with precision and context.

Take, for example, an application developer who notices high request duration for users who are trying to interact with their application. This application communicates with many different services, and it happens to run within a container environment on public cloud infrastructure. There are more than 50 possible root causes that might be causing the high request duration issue. That developer would need to investigate garbage collection issues, disk congestion, app-locking problems, and node congestion among many other potential root causes until accurately determining that a congested database is the source of their problem. The only proper way to determine root cause is by considering all the cause-and-effect relationships between all the possible root causes and the symptoms they may cause. This process can often take hours or days before the correct root cause is pinpointed, resulting in a variety of business consequences (unhappy users, missed SLOs, SLA violations, etc.).

In this post, we’ll explore the challenges of multi-team escalations, and the capabilities needed to address them. From automated dependency mapping to explainable triage workflows, we’ll show how observability can be transformed from chaos into clarity, making escalations less contentious and far more productive.

Escalations can cripple teams

Escalations create inefficiencies that extend downtime, frustrate teams, and waste resources. These inefficiencies stem from a combination of structural and technical gaps in how dependencies are understood, root causes are isolated, and ownership is assigned. Here are some of the key challenges that make escalations so painful today:

• There is a lack of cross-team visibility into dependencies
• It can be hard to predict or analyze the performance behaviors of loosely coupled dependent microservices
• It can be difficult to isolate the root cause among all affected services
• Legacy observability tools must be stitched together to provide even partial visibility into issues

Lack of cross-team visibility

Microservices architectures are complex and full of deeply interconnected components. An issue in one can cascade into others. Without clear visibility into these dependencies, teams are left guessing which components are impacted and which team should take ownership.

Your favorite observability tools help you visualize dependencies, but they lack real-time accuracy. These maps can quickly become outdated in environments with frequent changes. Some of them are great for aggregating logs, but don’t offer much insight into service relationships. Engineers are often left to piece together dependencies manually.

Unpredictable performance behavior of microservices

Loosely coupled microservices communicate with each other and share resources. But which services depend on which? And what resources are shared by which services? These dependencies are continuously changing and, in many cases, unpredictable.

A congested database may cause performance degradations of some services that are accessing the database. But which one will be degraded? Hard to know. Depends. Which services are accessing which tables through what APIs? Are all tables or APIs impacted by the bottleneck? Which other services depend on the services that are degraded? Are all of them going to be degraded? These are very difficult questions to answer.

As a result, predicting, understanding and analyzing the performance behavior of each service is very difficult. Using existing brittle observability tools to diagnose how a bottleneck cascades across services is practically impossible.

Difficulty identifying root causes among all affected services

Determining what’s a cause and what’s a symptom can be an incredibly time-consuming aspect of troubleshooting and escalations. Further, the person or team identifying a problem may well be looking at only their local maxima: the part of the system they work on or are directly affected by. They often don’t see the full picture of all intertwined systems. Identifying the root cause among all affected services can be inordinately difficult.

Even if you have tools that are excellent for visualizing time-series data, you must still rely on engineers to manually correlate metrics. APM tools can help you examine application performance but require significant manual effort to link symptoms to underlying causes, especially in microservices-based, cloud-native applications.

Legacy observability tooling only gives you partial functionality

While both established and up-and-coming tools offer valuable capabilities, they often address only one part of the problem, leaving critical gaps. Dependency visibility, performance analysis and root cause isolation need to be integrated seamlessly to reduce the chaos of escalations. Today’s tools, however, are fragmented, requiring engineers to bridge the gaps manually, costing valuable time and effort during incidents. Solving these problems demands a holistic approach that ties all these elements together in real time.

How escalations should be handled

Escalations have negative consequences for organizations of all sizes. Let’s work together to build systems that render escalations less about blame and more about opportunities to foster trust and collaboration.

These systems will require certain capabilities, which are explained further in the full article here.