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Q&A: AppDynamics Talks About APM - Part 1

Pete Goldin
APMdigest

In Part 1 of APMdigest's exclusive interview, AppDynamics talks about Application Performance Management (APM), monitoring and the 2015 APM Tools Survey, conducted by Enterprise Management Associates (EMA). Bhaskar Sunkara is AppDynamics CTO and SVP of Product Management. Jonah Kowall is VP of Market Development and Insights at AppDynamics.

APM: A recent study by EMA for AppDynamics found organizations have more than 10 monitoring products. What causes this situation?

Kowall: IT is faced with many challenges and numerous technology shifts, which occur regularly. As technology evolves, management must follow. Ultimately, as vendors create technologies, it is in their best interest to attach management tools to products in order to create stickiness and enable continued adoption and growth within the organization. The proliferation of these tools has created challenges within IT as they isolate problems. These domain specific or siloed tools don’t provide the right level of cross technology visibility.

APM: What is the downside of having so many tools? Why doesn't 10x tools = 10x visibility?

Sunkara: There are several downsides to having so many tools, such as having too much data to process without having enough context. When you have 10 tools, you have to watch 10 times the metrics. Not only is this ineffective, but it also doesn’t help determine the user experience is, as it doesn’t tie back.

Kowall: Ultimately, IT needs to better align to the business. The business consumes apps, not infrastructure. If monitoring and visibility provides businesses with infrastructure metrics and depth, you are not going to have the alignment with your business. IT organizations are realizing this and shifting spending away from infrastructure-centric tooling and visibility towards users and applications in order to meet business demand.

APM: What is the ideal number of tools?

Kowall: Ideally, you should not be focused on reducing the number of tools, but more importantly on building tools which have context back to that user and application. Today, companies need at least a dozen tools, unfortunately. AppDynamics can replace a lot of fragmented tools across APM, end user experience monitoring, synthetic transactions, server monitoring, database monitoring, and more. Over time, our goal is to replace even more tooling with our  unified monitoring software, which is a single user interface, a single install, and is the same software deployed SaaS or on-premises.

APM: How does an IT organization go from 10+ tools to a tighter selection?

Sunkara: The key is to start looking at how to streamline workflows. If you have an alerting workflow with your application and your infrastructure is unhealthy, start pushing alerts based on metrics from one common console to start consolidating. Most tools have some sort of overlap, so the next thing you should be focused on is figuring out where to start removing overlap. Be aware that teams will want to hang on to their tools, so you’ll need to make choices and understand advantages of the innovative tools versus legacy offerings. By being conscientious and working on consolidation, you’ll be able to streamline and cut down on workflows.

APM: The same report said that MTTR still takes many organizations several hours, with multiple people working on the issue. What are they doing wrong?

Kowall: This goes back to same issue around context. Each silo has its own view of a specific sliver of the application or infrastructure. Problem isolation takes far too long and the result is extended amounts of downtime. The two most recent occurrences with widespread press include the United Airlines and New York Stock Exchange outages. Each example resulted in multiple hours of downtime to isolate the issue and correct it. For United, the impact of its outage included hundreds of canceled and delayed flights — and thousands of upset passengers. NYSE’s issues impacted billions of trades, which could not be executed, as well as brand damage and upset customers.

Read Q&A: AppDynamics Talks About APM - Part 2

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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? ...

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 ...

Q&A: AppDynamics Talks About APM - Part 1

Pete Goldin
APMdigest

In Part 1 of APMdigest's exclusive interview, AppDynamics talks about Application Performance Management (APM), monitoring and the 2015 APM Tools Survey, conducted by Enterprise Management Associates (EMA). Bhaskar Sunkara is AppDynamics CTO and SVP of Product Management. Jonah Kowall is VP of Market Development and Insights at AppDynamics.

APM: A recent study by EMA for AppDynamics found organizations have more than 10 monitoring products. What causes this situation?

Kowall: IT is faced with many challenges and numerous technology shifts, which occur regularly. As technology evolves, management must follow. Ultimately, as vendors create technologies, it is in their best interest to attach management tools to products in order to create stickiness and enable continued adoption and growth within the organization. The proliferation of these tools has created challenges within IT as they isolate problems. These domain specific or siloed tools don’t provide the right level of cross technology visibility.

APM: What is the downside of having so many tools? Why doesn't 10x tools = 10x visibility?

Sunkara: There are several downsides to having so many tools, such as having too much data to process without having enough context. When you have 10 tools, you have to watch 10 times the metrics. Not only is this ineffective, but it also doesn’t help determine the user experience is, as it doesn’t tie back.

Kowall: Ultimately, IT needs to better align to the business. The business consumes apps, not infrastructure. If monitoring and visibility provides businesses with infrastructure metrics and depth, you are not going to have the alignment with your business. IT organizations are realizing this and shifting spending away from infrastructure-centric tooling and visibility towards users and applications in order to meet business demand.

APM: What is the ideal number of tools?

Kowall: Ideally, you should not be focused on reducing the number of tools, but more importantly on building tools which have context back to that user and application. Today, companies need at least a dozen tools, unfortunately. AppDynamics can replace a lot of fragmented tools across APM, end user experience monitoring, synthetic transactions, server monitoring, database monitoring, and more. Over time, our goal is to replace even more tooling with our  unified monitoring software, which is a single user interface, a single install, and is the same software deployed SaaS or on-premises.

APM: How does an IT organization go from 10+ tools to a tighter selection?

Sunkara: The key is to start looking at how to streamline workflows. If you have an alerting workflow with your application and your infrastructure is unhealthy, start pushing alerts based on metrics from one common console to start consolidating. Most tools have some sort of overlap, so the next thing you should be focused on is figuring out where to start removing overlap. Be aware that teams will want to hang on to their tools, so you’ll need to make choices and understand advantages of the innovative tools versus legacy offerings. By being conscientious and working on consolidation, you’ll be able to streamline and cut down on workflows.

APM: The same report said that MTTR still takes many organizations several hours, with multiple people working on the issue. What are they doing wrong?

Kowall: This goes back to same issue around context. Each silo has its own view of a specific sliver of the application or infrastructure. Problem isolation takes far too long and the result is extended amounts of downtime. The two most recent occurrences with widespread press include the United Airlines and New York Stock Exchange outages. Each example resulted in multiple hours of downtime to isolate the issue and correct it. For United, the impact of its outage included hundreds of canceled and delayed flights — and thousands of upset passengers. NYSE’s issues impacted billions of trades, which could not be executed, as well as brand damage and upset customers.

Read Q&A: AppDynamics Talks About APM - Part 2

The Latest
The Latest 10

The Latest

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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? ...

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 ...