Digital transformation has accelerated innovation across industries, driving organizations to adopt cloud technologies, containers, microservices, and AI in their operations. However, this expanding digital ecosystem also brings new challenges — increasingly complex systems, the need for broader visibility, and the difficulty of maintaining consistent application performance across diverse environments. 

In this context, the need for AI-powered observability emerges — a modern approach that combines artificial intelligence and automation to deliver end-to-end visibility, quickly identify root causes, and help organizations maintain system stability in the multi-cloud and AI-driven era. 

What Is AI Observability?

AI observability is a modern approach that leverages artificial intelligence to analyze data across all layers of applications, infrastructure, and user experience. 

Unlike traditional observability that focuses only on anomaly detection, AI observability understands the causal relationships between system components. As a result, IT teams can automatically identify the root cause of issues, accelerate incident recovery, and minimize business impact. 

The Complexity of Digital Environments in the Multi-Cloud and Microservices Era

Modern organizations operate in highly dynamic hybrid and multi-cloud environments. Applications are distributed across multiple platforms, with complex dependencies among services, APIs, and data pipelines. 

This creates new challenges: an overwhelming volume of information that’s difficult to analyze manually, fragmented observability tools, and a lack of context connecting system performance to business impact. Without an integrated platform, observability can become a new source of confusion. 

The Challenges of Modern Observability

Today’s IT teams face three main challenges: 

  • Tool sprawl — caused by too many disconnected observability solutions. 
  • Overload — due to the growing volume of logs, metrics, and traces. 
  • Lack of AI integration — leading to reactive rather than proactive monitoring. 

To overcome these issues, organizations need a unified observability platform capable of understanding context, predicting potential problems, and providing relevant insights automatically. 

Dynatrace AI Observability: Tackling Complexity with Artificial Intelligence 

Dynatrace introduces Davis AI — a causal AI engine that automatically and accurately identifies the root cause of incidents. 

With its context-based, cause-and-effect analysis, Davis AI doesn’t just report anomalies — it explains why and how issues happen. This enables IT teams to act faster and more effectively, reducing downtime and improving overall system reliability. 

In addition, the Dynatrace platform supports end-to-end observability across the entire ecosystem — from mainframes to cloud-native applications, and from apps to AI models — all within a unified data model. 

Key Features of Dynatrace AI Observability

Causal AI-Powered Observability  

Davis AI automatically analyzes interdependencies between systems and finds the root cause of problems without manual intervention. <h3>End-to-End Visibility</h3> Dynatrace provides complete observability across applications, infrastructure, and user experiences — spanning hybrid, multi-cloud, and AI environments.  

Automatic Discovery and Instrumentation 

With OneAgent, every component and dependency is automatically detected in real time, eliminating the need for complex manual setups.  

Predictive and Proactive Intelligence  

AI-driven anomaly detection anticipates issues before they affect end users, enabling faster preventive action.  

BizDevSecOps Alignment  

Dynatrace integrates observability insights with business and security context, enhancing cross-team collaboration and accelerating innovation.  

Unified Platform for Observability, Security, and Automation

All functions — from observability to security and automation — are available within one unified, scalable, and easy-to-manage platform.  

Benefits of Using Dynatrace AI Observability

Complete Visibility from Applications to Infrastructure

Monitor every layer of your digital ecosystem — from front-end and back-end applications to AI infrastructure and LLM models — in real time.  

Operational Efficiency and Cost Reduction

Through automation and AI, Dynatrace reduces manual monitoring tasks and eliminates the cost of managing multiple disjointed observability tools.  

Faster Incident Detection and Resolution  

Causal AI allows faster identification and resolution of incidents — improving uptime and ensuring seamless user experiences.  

Future-Ready Observability for AI Workloads  

Dynatrace supports observability for Agentic AI, Generative AI, and LLMs with native integration into leading AI platforms.  

Explore Dynatrace on Virtus here. 

Building the Future of Observability with Dynatrace and Virtus

As the authorized distributor of Dynatrace in Indonesia, Virtus Technology Indonesia empowers organizations to build intelligent, integrated, and AI-driven observability. 

With extensive expertise in data center, cloud, and cybersecurity, Virtus guides businesses through every stage of implementation — from integration to performance optimization. 

Partner with Virtus and Dynatrace to transform your observability strategy and build a smarter, more efficient digital ecosystem ready for the future of AI. 

Author: Ary Adianto
Content Writer, CTI Group