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The New APM All-Stars: Automation and Artificial Intelligence

Chris Farrell

With the NBA All-Star game just ahead and the NHL game just behind, it made me consider the All-Stars of Application Monitoring — Automation and Artificial Intelligence (AI).

Automation is the workhorse that does all the little things: In basketball terms, that means rebounding and setting screens. For hockey, that's killing penalties and floor-checking.

For APM, "Automation" supports tasks like Discovery, Setup, Configuration, Reporting.

Then there's the superstar playmaker — the player that sees the whole field of play, understands what will happen beforehand and places resources in the right position to make the winning plays. For the new generation of APM, that's AI.

AI sees everything; analyzes everything. AI recognizes strengths and weaknesses. AI sees events and problems before they actually occur and takes actions as needed to address the situation.

A Brave New World

Today's digitally transformed organizations have embraced Agile and DevOps to help them quickly and efficiently deploy dynamic applications using containers and microservice architectures. Developing applications at an increasingly rapid pace with these new technologies helps businesses succeed, but it adds tremendous complexity to the application and IT environments.

Automation and AI technologies are critical to the next step in APM evolution

Operations teams can no longer manually monitor and manage today's applications due to their dynamic and complex nature, as well as the speed of development and deployment. APM had to evolve to keep pace with development velocity and maintain the service quality for the modern applications born out of digital transformation.

Automation and artificial intelligence (AI) technologies are critical to the next step in APM evolution, helping to address speed, scalability and intelligence demands.

Automation and AI are Better Together

On basketball and hockey teams, when each star does their job together with their teammates, it's a winning combination. APM is no different when AI and Automation get together. Unlike a sports team, though, "losing a game" for APM owners can be catastrophic.

Today's businesses ARE their applications. If an application isn't running well, users (employees, partners, or customers) are unhappy and the business suffers. If an application completely breaks, it can severely impact the bottom line.

For APM to work, it needs more than data. It needs actionable information to analyze and manage application performance.

Early APM tools did pretty well managing applications that were 3-tiered monoliths or SOA-based. But with the growth of cloud, microservices and containers, monitoring and managing today's applications requires increased speed, scalability and intelligence.

Unfortunately, the development velocity (approaching continuous delivery) and application complexity make it impossible for humans (even experts) to analyze all the necessary information and take appropriate actions to keep applications performing properly.

Enter the two new APM all-stars — AI and Automation — the only way for any organization to regain control over their complex applications. Why are both of these great concepts required, and required to work together?

1. To match the speed and scalability of the modern dynamic applications

2. To analyze the massive amounts of data gathered and measured from each application

3. To take appropriate actions to adjust the infrastructure, platform or microservices to ensure the application performs as needed

Proper automation sets the stage for AI to analyze and act on application performance through:

Continuous discovery and mapping: Proper application of AI requires completely accurate data, especially dependency information. Otherwise, you're wasting time analyzing bad data. With automated, continuous discovery, application maps are always accurate and always reflect reality.

To deliver actionable information, AI has to start with Hi-Fidelity data

Hi-Fidelity data: To deliver actionable information, AI has to start with Hi-Fidelity data. After automatically discovering the components and mapping the application, monitoring can begin. But the information must be in real-time. As fast as applications change, operations teams need to those updates — in one second or less. This ensures AI is applied to the most accurate data possible.

A full stack application model: Because true AI can only operate with the deepest visibility, understanding the dependencies and configurations among the physical and logical components — in real-time — is critical.

Accommodating container and microservice fluidity: Modern applications are in constant change, are deployed in hybrid environments and will likely include multiple programming languages, middleware and databases, especially if using microservices. Automated discovery with zero configuration will help operations keep pace with development teams and application change.

With automation providing up-to-the-second, high-quality data; a full understanding of the application component dependencies; and the ability to handle modern development and deployment paradigms such as microservices and containers, AI confidently can be engaged to provide:

Real-time, AI-driven incident monitoring and prediction: DevOps teams need accurate and actionable information within seconds after change occurs. That type of information also is invaluable when a performance problem is imminent. Applying real-time AI to the monitoring data, which is coming in real-time, delivers analysis and performance insights in less than three seconds.

AI-powered troubleshooting and resolution: Modern applications are simply too complex for humans to effectively manage on their own. Because of the dynamic nature of their structure, their complex dependencies and the sheer scale, machines learning and AI must be applied. The application intelligence generated provides a baseline for problem prediction and resolution.

Automation and AI working together (and operating in seconds, not minutes) are the core components for managing modern applications. They can combine to create a democratized APM solution capable of handling the speed, scalability and intelligence that today's dynamic applications require.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

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Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

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The New APM All-Stars: Automation and Artificial Intelligence

Chris Farrell

With the NBA All-Star game just ahead and the NHL game just behind, it made me consider the All-Stars of Application Monitoring — Automation and Artificial Intelligence (AI).

Automation is the workhorse that does all the little things: In basketball terms, that means rebounding and setting screens. For hockey, that's killing penalties and floor-checking.

For APM, "Automation" supports tasks like Discovery, Setup, Configuration, Reporting.

Then there's the superstar playmaker — the player that sees the whole field of play, understands what will happen beforehand and places resources in the right position to make the winning plays. For the new generation of APM, that's AI.

AI sees everything; analyzes everything. AI recognizes strengths and weaknesses. AI sees events and problems before they actually occur and takes actions as needed to address the situation.

A Brave New World

Today's digitally transformed organizations have embraced Agile and DevOps to help them quickly and efficiently deploy dynamic applications using containers and microservice architectures. Developing applications at an increasingly rapid pace with these new technologies helps businesses succeed, but it adds tremendous complexity to the application and IT environments.

Automation and AI technologies are critical to the next step in APM evolution

Operations teams can no longer manually monitor and manage today's applications due to their dynamic and complex nature, as well as the speed of development and deployment. APM had to evolve to keep pace with development velocity and maintain the service quality for the modern applications born out of digital transformation.

Automation and artificial intelligence (AI) technologies are critical to the next step in APM evolution, helping to address speed, scalability and intelligence demands.

Automation and AI are Better Together

On basketball and hockey teams, when each star does their job together with their teammates, it's a winning combination. APM is no different when AI and Automation get together. Unlike a sports team, though, "losing a game" for APM owners can be catastrophic.

Today's businesses ARE their applications. If an application isn't running well, users (employees, partners, or customers) are unhappy and the business suffers. If an application completely breaks, it can severely impact the bottom line.

For APM to work, it needs more than data. It needs actionable information to analyze and manage application performance.

Early APM tools did pretty well managing applications that were 3-tiered monoliths or SOA-based. But with the growth of cloud, microservices and containers, monitoring and managing today's applications requires increased speed, scalability and intelligence.

Unfortunately, the development velocity (approaching continuous delivery) and application complexity make it impossible for humans (even experts) to analyze all the necessary information and take appropriate actions to keep applications performing properly.

Enter the two new APM all-stars — AI and Automation — the only way for any organization to regain control over their complex applications. Why are both of these great concepts required, and required to work together?

1. To match the speed and scalability of the modern dynamic applications

2. To analyze the massive amounts of data gathered and measured from each application

3. To take appropriate actions to adjust the infrastructure, platform or microservices to ensure the application performs as needed

Proper automation sets the stage for AI to analyze and act on application performance through:

Continuous discovery and mapping: Proper application of AI requires completely accurate data, especially dependency information. Otherwise, you're wasting time analyzing bad data. With automated, continuous discovery, application maps are always accurate and always reflect reality.

To deliver actionable information, AI has to start with Hi-Fidelity data

Hi-Fidelity data: To deliver actionable information, AI has to start with Hi-Fidelity data. After automatically discovering the components and mapping the application, monitoring can begin. But the information must be in real-time. As fast as applications change, operations teams need to those updates — in one second or less. This ensures AI is applied to the most accurate data possible.

A full stack application model: Because true AI can only operate with the deepest visibility, understanding the dependencies and configurations among the physical and logical components — in real-time — is critical.

Accommodating container and microservice fluidity: Modern applications are in constant change, are deployed in hybrid environments and will likely include multiple programming languages, middleware and databases, especially if using microservices. Automated discovery with zero configuration will help operations keep pace with development teams and application change.

With automation providing up-to-the-second, high-quality data; a full understanding of the application component dependencies; and the ability to handle modern development and deployment paradigms such as microservices and containers, AI confidently can be engaged to provide:

Real-time, AI-driven incident monitoring and prediction: DevOps teams need accurate and actionable information within seconds after change occurs. That type of information also is invaluable when a performance problem is imminent. Applying real-time AI to the monitoring data, which is coming in real-time, delivers analysis and performance insights in less than three seconds.

AI-powered troubleshooting and resolution: Modern applications are simply too complex for humans to effectively manage on their own. Because of the dynamic nature of their structure, their complex dependencies and the sheer scale, machines learning and AI must be applied. The application intelligence generated provides a baseline for problem prediction and resolution.

Automation and AI working together (and operating in seconds, not minutes) are the core components for managing modern applications. They can combine to create a democratized APM solution capable of handling the speed, scalability and intelligence that today's dynamic applications require.

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...