Skip to main content

Slow Applications Are Criminal

Larry Dragich

In the world of Application Performance Management (APM) it is always better to enlist more than one entity to help solve the mystery of performance problems.

It's kind of like arriving at the scene of the crime on foreign soil, being blindfolded, shoved out the door, and then asked to help solve the injustice without any insight. All you can do is begin by asking people in the vicinity, providing you speak their language, for information on what they have seen (i.e. end-user-experience).

Gathering facts related to a crime is essential, and can be likened to utilizing an APM solution for solving application performance problems. The more information about an application’s behavior that you can obtain, along with understanding its idiosyncrasies within the environment, the more likely you will be able to pinpoint root causes of performance issues.

The Three People You Need

Wouldn't it be helpful if there was an eye witness you could interview, a watchman who was on duty during the time of the incident, and an agent you could hire to translate the native tongue and provide insight into the culture?

In much the same way, a smart APM strategy enlists the help from these three entities: the Witness, the Watchman, and the Agent. You start by listening to the testimony from the eye witness (aka. wire data), collecting the observations from the watchman (aka. web robots), and analyzing details from the agent (aka. code level instrumentation).

The Witness

Passive monitoring, wire-data analytics

The Witness reports what they see within their field of vision, (aka. passive monitoring, wire-data analytics). The Witness is watching everything in their purview and sees things as they happen, which corresponds to what is coming across "the wire" in front of them.

The Witness will tell you how many people were involved, if anyone was injured, and what time the event occurred, (e.g. user names, packet loss, timelines, etc.). She can tell you what doors the people went through, how wide the aisles were, and how fast people were traveling, (e.g. network port listeners, realized bandwidth, round-trip-time, etc.).

The Watchman

Active monitoring - synthetic transactions

The Watchman (aka. web robot) is actively checking and is always on patrol, methodically taking the same path every time. He will tell you what doors are locked and monitor the ones that are open, collecting measurements along the way on how long it takes to complete his rounds, (i.e. synthetic transactions).

The Watchman will report the status of the rooms and buildings on his patrol and will note if anything happens to him along the way, (e.g. application availability, transaction errors, timeouts, etc.).

The Agent

Application code instrumentation

The Agent you hire is critical for solving the crime within the territory you're operating in. The Agent will watch activity from specific vantage points throughout the environment and report back his findings. It's crucial he speaks the local language, (e.g. Java, .Net, PHP) and can easily translate for you.

His approach will be to deploy probes on rooftops and inside the buildings for monitoring all conversations and actions in the environment, (aka. application code instrumentation). He will also tap the communication systems, (i.e. script injection) when appropriate and capture specific measurements from each conversation and record them.   

Going from Red to Green

Identifying an application that has gone catatonic is one thing, but assessing the insidious slow performance of a complex multi-tiered application and fixing it, can be very time consuming and costly. Enlisting all three entities described above to assist is a thoughtful strategy for any IT Leader to consider.

Based on eye witness testimony, the forensics collected, and the conversations recorded, you will be well on your way to providing an accurate account of what has transpired and why, (i.e. root cause analysis).

Conclusion

Remember, the end-user is the supreme judge in this case and if performance is chronically slow, your sentence could be harsh. Either directly by inundating you with complaints creating bad press or indirectly by abandoning your site in favor of one that is much faster and more intuitive to use.

Embracing a smart but simple APM Methodology within your environment may be the only thing that exonerates you when the verdict for your slow application is "guilty as charged."

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Slow Applications Are Criminal

Larry Dragich

In the world of Application Performance Management (APM) it is always better to enlist more than one entity to help solve the mystery of performance problems.

It's kind of like arriving at the scene of the crime on foreign soil, being blindfolded, shoved out the door, and then asked to help solve the injustice without any insight. All you can do is begin by asking people in the vicinity, providing you speak their language, for information on what they have seen (i.e. end-user-experience).

Gathering facts related to a crime is essential, and can be likened to utilizing an APM solution for solving application performance problems. The more information about an application’s behavior that you can obtain, along with understanding its idiosyncrasies within the environment, the more likely you will be able to pinpoint root causes of performance issues.

The Three People You Need

Wouldn't it be helpful if there was an eye witness you could interview, a watchman who was on duty during the time of the incident, and an agent you could hire to translate the native tongue and provide insight into the culture?

In much the same way, a smart APM strategy enlists the help from these three entities: the Witness, the Watchman, and the Agent. You start by listening to the testimony from the eye witness (aka. wire data), collecting the observations from the watchman (aka. web robots), and analyzing details from the agent (aka. code level instrumentation).

The Witness

Passive monitoring, wire-data analytics

The Witness reports what they see within their field of vision, (aka. passive monitoring, wire-data analytics). The Witness is watching everything in their purview and sees things as they happen, which corresponds to what is coming across "the wire" in front of them.

The Witness will tell you how many people were involved, if anyone was injured, and what time the event occurred, (e.g. user names, packet loss, timelines, etc.). She can tell you what doors the people went through, how wide the aisles were, and how fast people were traveling, (e.g. network port listeners, realized bandwidth, round-trip-time, etc.).

The Watchman

Active monitoring - synthetic transactions

The Watchman (aka. web robot) is actively checking and is always on patrol, methodically taking the same path every time. He will tell you what doors are locked and monitor the ones that are open, collecting measurements along the way on how long it takes to complete his rounds, (i.e. synthetic transactions).

The Watchman will report the status of the rooms and buildings on his patrol and will note if anything happens to him along the way, (e.g. application availability, transaction errors, timeouts, etc.).

The Agent

Application code instrumentation

The Agent you hire is critical for solving the crime within the territory you're operating in. The Agent will watch activity from specific vantage points throughout the environment and report back his findings. It's crucial he speaks the local language, (e.g. Java, .Net, PHP) and can easily translate for you.

His approach will be to deploy probes on rooftops and inside the buildings for monitoring all conversations and actions in the environment, (aka. application code instrumentation). He will also tap the communication systems, (i.e. script injection) when appropriate and capture specific measurements from each conversation and record them.   

Going from Red to Green

Identifying an application that has gone catatonic is one thing, but assessing the insidious slow performance of a complex multi-tiered application and fixing it, can be very time consuming and costly. Enlisting all three entities described above to assist is a thoughtful strategy for any IT Leader to consider.

Based on eye witness testimony, the forensics collected, and the conversations recorded, you will be well on your way to providing an accurate account of what has transpired and why, (i.e. root cause analysis).

Conclusion

Remember, the end-user is the supreme judge in this case and if performance is chronically slow, your sentence could be harsh. Either directly by inundating you with complaints creating bad press or indirectly by abandoning your site in favor of one that is much faster and more intuitive to use.

Embracing a smart but simple APM Methodology within your environment may be the only thing that exonerates you when the verdict for your slow application is "guilty as charged."

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...