AI-Driven Application Performance Management(APM).

Mar 1, 2021. By Aleena Mathew

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AI-Driven Application Performance Management(APM)

Business demands increase the number of applications in the enterprise IT application portfolio. This proliferation calls for the process of effectively monitoring every application that is present in the system. That's where Application Performance Monitoring(APM) comes into the picture.

Application Performance Management(APM):

APM refers to any tool designed to monitor application availability and performance to optimize the user experience and maximize application efficiency. Observing application performance is a crucial factor in every IT organization as it solely helps in understanding if any application is working or not up to the required level. APM can help in identifying any server crash by pinging the status or performance issues of an application. Moreover, application tracing and diagnostic are made possible.

However, with all the noise around APM, there are still challenges and difficulties in coping with the current digital era. Digital transformation has shaken up the entire IT organization. The traditional system was long gone with the evolvement of modern technology. The conventional IT setup could not handle the current IT requirements necessary to meet the business requirements. That's where digital transformation took its place. But with this change in spot, traditional APM was just not enough. The primary ability of traditional APM was to identify any outliers in the system or give the root cause of the issue if any application failed. In general, we can say that the conventional APM tool was just reactive in-nature. They just used to provide some alerts on any failures. That particular scenario ultimately leads to a situation where the business needs to adjust to the end-user requirements. The actual need here is to have a proactive and predictive APM.

So what does proactive APM mean here? Proactively means to predict the application performance problem before they occur in the system. Altering alone won't make the system proactive. We need to identify any correlation for the alerts that are happening in the context of the problem. This is to identify the root cause of the issue. To evolve to such a proactive environment requires the assistance of new generation technology like Artificial Intelligence. AI enables the traditional APM to evolve to the next level. The implementation of AI-based APM gained the potential to perform intelligent data-based assessments of complex application environments, understand unknown application issues, also enable automated, real-time response to many application performance issues.

AI-Based Application Performance Management:

Unlocking the capabilities of AI for APM, AI-based deep observability is made possible. Using advanced AI-based observability, the application performance team can get much more insights than traditional APM tools. The modern AI-based tools can leverage structured and unstructured data, including KPIs, logs, events, etc... Based on this, an AI-based APM system will intelligently recognize if there is an unknown problem in the system. Mostly, it helps in identifying the root causes of any problem. The system will also perform an AI-based anomaly detection mechanism to uncover any anomaly in the system. When such an event or anomaly occurs, the system will automatically alert the respected APM user and automatically initiate an auto-remediation of the incidents. In this way, whenever an issue happens, the AI enable APM system is handling their problems and resolving them automatically before it even impacts the smooth flow of business. It eventually improves the end-user experience because whenever an issue occurs, it will get automatically resolved before even impacting the end-users.

Key benefits of AI-enabled Application Performance Management:

With all that in hand, let's see some of the major benefits AI-enabled APM supports the system.

1.Anomaly Detection Mechanism:

The key benefit of AI is the anomaly detection mechanism. Anomaly detection, also known as outliers analysis, helps identify unknown problems from a huge volume of application performance and transaction data. From these data, any outliers that are out of their normal behavior will be automatically identified. Once such abnormal behavior is found, it will be intelligently alerted.

2.Root Cause Analysis:

Traditional APM tools provide a lot of events and false-positive events. There are instances where incidents can occur due to the break of some components. Finding genuine issues from the event noises is a challenging task. That's where the AI-based root cause analysis mechanism plays its role. It helps identify the correlation among multiple KPIs, events, and logs and provides the right reason for why an event occurred.

3.Improving End-User Experience:

This is a critical factor for any business. If they can retain their end-users, then only a business can run. For this to happen, the applications should run smoothly on the user side. AI-enabled APM mechanism enables application teams to predict the end-user experience issues and addresses them before they get customer complaints. Thus the AI-based APM effectively ensures that the smooth run of the applications is maintained.

4.Transaction Monitoring:

In today's digital business world, application owners need to see how end-user interact effectively with their applications and monitor the corresponding internal transactions. AI can improve transaction monitoring that detects real-time issues and pinpoints the root causes, and triggers auto-remediation. It also helps in measuring user experience and effectiveness of alerts when a business transaction response time is low.


Apdex is an open standard used to measure the user satisfaction of an application. It provides a metric that shows how much the user is satisfied with the application that is used. Based on the apdex score, APM tools can evaluate how the application is performing. With the AI-enabled APM tool, we can easily predict the index score. Based on this prediction, we can proactively predict if any applications will face issues. In this way, we can easily auto-remediate the situation even before it impacts the business and end-user.

To learn more about Algomox AIOps please visit our AIOps Platform Page.

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