Keep Your Application Monitoring Out of the Dark Ages

The right blend of APM and microservices can bring your organization into the enlightened age
Matthew Dubie
Learn more about Broadcom

Let's go back in time. Think of when your applications used to run from a single server and when the monolithic enterprise management software approach was more than enough to effectively monitor them. I know those days may have been just 10 years ago, but given the fast pace of the tech industry, those are officially our dark ages.

Now, let's fast-forward to the present application economy in which your customers are demanding higher quality applications faster than ever before. To meet these new expectations, the infrastructure of the application has evolved; inevitably becoming more sophisticated and ultimately more complex.

The complexity begins with the microservices architecture, which is the way many of today's enterprise applications are built. Microservices compartmentalize the application by function. Each function within the application architecture focuses on performing a small, specific process and communicates with other functions using APIs. This differs from the traditional service-oriented architecture (SOA), in that SOAs work to integrate multiple applications that function independently to perform a service.

Why Complicate Things?

The more an app can do, the better. Customers expect more than ever of enterprise applications — they want them to perform like consumer apps do — which results in an added pressure on organizations to be agile. Microservices do just that. By dividing application functions across the architecture, developers are better able to resolve issues and make adjustments more quickly — without having to redeploy the entire application.

Just as with the architecture, the monolithic approach of application monitoring that used to work is no longer sufficient. Microservices are more granular than SOAs and introduce a variety of new monitoring challenges that require an application monitoring approach better able to manage the more sophisticated application environment.

The four main challenges microservices present to application monitoring are complexity, change, resiliency and scale. These new intricacies make it difficult for application monitoring solutions to pinpoint the source where application issues arise, monitor environments at the rate in which they change, triage alerts, and scale the large amounts of data.

Your Apps Are Your Business

Microservices provide the functionality end users are looking for in their applications and your application monitoring solutions need to keep those applications up and running – and performing as customers demand.

But, the old approach to application monitoring just isn't working. It's time to forget about the dark ages; success in the application economy starts with providing your customers with a superior application experience. Is your application performance management approach enlightened?

Matthew Dubie is a Marketing Associate at CA Technologies.

Related Links

Hot Topics

Related Vendors

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Keep Your Application Monitoring Out of the Dark Ages

The right blend of APM and microservices can bring your organization into the enlightened age
Matthew Dubie
Learn more about Broadcom

Let's go back in time. Think of when your applications used to run from a single server and when the monolithic enterprise management software approach was more than enough to effectively monitor them. I know those days may have been just 10 years ago, but given the fast pace of the tech industry, those are officially our dark ages.

Now, let's fast-forward to the present application economy in which your customers are demanding higher quality applications faster than ever before. To meet these new expectations, the infrastructure of the application has evolved; inevitably becoming more sophisticated and ultimately more complex.

The complexity begins with the microservices architecture, which is the way many of today's enterprise applications are built. Microservices compartmentalize the application by function. Each function within the application architecture focuses on performing a small, specific process and communicates with other functions using APIs. This differs from the traditional service-oriented architecture (SOA), in that SOAs work to integrate multiple applications that function independently to perform a service.

Why Complicate Things?

The more an app can do, the better. Customers expect more than ever of enterprise applications — they want them to perform like consumer apps do — which results in an added pressure on organizations to be agile. Microservices do just that. By dividing application functions across the architecture, developers are better able to resolve issues and make adjustments more quickly — without having to redeploy the entire application.

Just as with the architecture, the monolithic approach of application monitoring that used to work is no longer sufficient. Microservices are more granular than SOAs and introduce a variety of new monitoring challenges that require an application monitoring approach better able to manage the more sophisticated application environment.

The four main challenges microservices present to application monitoring are complexity, change, resiliency and scale. These new intricacies make it difficult for application monitoring solutions to pinpoint the source where application issues arise, monitor environments at the rate in which they change, triage alerts, and scale the large amounts of data.

Your Apps Are Your Business

Microservices provide the functionality end users are looking for in their applications and your application monitoring solutions need to keep those applications up and running – and performing as customers demand.

But, the old approach to application monitoring just isn't working. It's time to forget about the dark ages; success in the application economy starts with providing your customers with a superior application experience. Is your application performance management approach enlightened?

Matthew Dubie is a Marketing Associate at CA Technologies.

Related Links

Hot Topics

Related Vendors

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...