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The App-Hugger's Brief History of Application Recovery - Part I: Pre-APM

Kevin McCartney

Here is a brief summary of the most common approaches to application recovery since the mid-1990s, along with an overview of the limitations we’ve run across most frequently.

METHOD: Scripting

DATES: 1995 – Present

ALSO KNOWN AS: “Manual Labor”

WHAT IT DOES:

• Users identify problems and alert IT

• IT focused on infrastructure, not apps – at this time, there was a direct correlation between server and app, as all apps ran on dedicated HW (prior to virtualization and cloud), which no longer exists

• Difficult to pinpoint problems

• Heavy reliance on scripts--requires maintenance of script library

METHOD: Runbooks

DATES: 2001 – Present

ALSO KNOWN AS: “The Manual Process of Manuals”

WHAT IT DOES:

• Shelves of binders: if this, then that

• IT still focused on infrastructure, not apps

• Still difficult to identify source of problems

• Recovery very labor intensive

METHOD: Runbook Automation

DATES: 2007 – Present

ALSO KNOWN AS: “Rise of the Machines”

WHAT IT DOES:

• Emergence of software platforms that can execute scripts

• Works for routine operations such as provisioning

• Still requires a manual decision on what to do (which runbook to execute) – as it lacks awareness of overall health or current state of an application

LIMITATIONS OF PRE-APM APPROACHES TO APPLICATION RECOVERY

IT organizations manage run-time applications largely through an infrastructure-centric approach (network, server monitoring), which is then used to derive application health. The challenge with the approach is that it is not application-aware, and cannot tell you anything of the critical applications running on top of them. In some cases, application level monitoring is implemented, which provides analytics about an application’s performance. However, without the ability to intelligently respond, or empower staff to do so, these analytics will have limited benefit to ensuring the uptime of applications in their run-time environment.These tools tend provide a historical or root cause analysis view, versus a responsive solution to addressing real-time issues.

In conjunction with this approach, IT organizations may couple monitoring with script-based tools , including (also known as Run Books,) to help improve the efficiency of routine and pre-defined tasks. Scripts and run books can be effective to automate basic tasks with a known “start” and “stop”, however, they are not well-suited, nor are they scalable for complex, run-time environments. This is due to the fact that to address run-time Application Management with this approach, it requires scripts to be written for every possible scenario, and every possible combination of scenarios that may occur for each application – and they must be continually updated and adapted as the environment grows.

Furthermore, this typically still requires manual decision-making. And if scripts are not run properly, based on the state, and in context of each application’s hierarchy and dependencies, they provide limited utility – and in cases may actually compound the application downtime and data corruption problems they sought to prevent.

The App Hugger's Brief History of Application Recovery - Part II: The APM Era

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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 ...

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The App-Hugger's Brief History of Application Recovery - Part I: Pre-APM

Kevin McCartney

Here is a brief summary of the most common approaches to application recovery since the mid-1990s, along with an overview of the limitations we’ve run across most frequently.

METHOD: Scripting

DATES: 1995 – Present

ALSO KNOWN AS: “Manual Labor”

WHAT IT DOES:

• Users identify problems and alert IT

• IT focused on infrastructure, not apps – at this time, there was a direct correlation between server and app, as all apps ran on dedicated HW (prior to virtualization and cloud), which no longer exists

• Difficult to pinpoint problems

• Heavy reliance on scripts--requires maintenance of script library

METHOD: Runbooks

DATES: 2001 – Present

ALSO KNOWN AS: “The Manual Process of Manuals”

WHAT IT DOES:

• Shelves of binders: if this, then that

• IT still focused on infrastructure, not apps

• Still difficult to identify source of problems

• Recovery very labor intensive

METHOD: Runbook Automation

DATES: 2007 – Present

ALSO KNOWN AS: “Rise of the Machines”

WHAT IT DOES:

• Emergence of software platforms that can execute scripts

• Works for routine operations such as provisioning

• Still requires a manual decision on what to do (which runbook to execute) – as it lacks awareness of overall health or current state of an application

LIMITATIONS OF PRE-APM APPROACHES TO APPLICATION RECOVERY

IT organizations manage run-time applications largely through an infrastructure-centric approach (network, server monitoring), which is then used to derive application health. The challenge with the approach is that it is not application-aware, and cannot tell you anything of the critical applications running on top of them. In some cases, application level monitoring is implemented, which provides analytics about an application’s performance. However, without the ability to intelligently respond, or empower staff to do so, these analytics will have limited benefit to ensuring the uptime of applications in their run-time environment.These tools tend provide a historical or root cause analysis view, versus a responsive solution to addressing real-time issues.

In conjunction with this approach, IT organizations may couple monitoring with script-based tools , including (also known as Run Books,) to help improve the efficiency of routine and pre-defined tasks. Scripts and run books can be effective to automate basic tasks with a known “start” and “stop”, however, they are not well-suited, nor are they scalable for complex, run-time environments. This is due to the fact that to address run-time Application Management with this approach, it requires scripts to be written for every possible scenario, and every possible combination of scenarios that may occur for each application – and they must be continually updated and adapted as the environment grows.

Furthermore, this typically still requires manual decision-making. And if scripts are not run properly, based on the state, and in context of each application’s hierarchy and dependencies, they provide limited utility – and in cases may actually compound the application downtime and data corruption problems they sought to prevent.

The App Hugger's Brief History of Application Recovery - Part II: The APM Era

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.