Observability 2.0: The Future of Monitoring with AI and Automation

Observability with Automation and AI is a concept that involves using automated tools and artificial intelligence to enhance observability in modern cloud environments. It is essential to have an expanded approach to observability in these environments, as they are complex and dynamic. Advanced observability requires automation, context, and AI to be effective. Real-time topology mapping is a crucial element of advanced observability, as it provides context across the entire stack. Additionally, automation is required for scalability and completeness. By using observability with automation and AI, organizations can more effectively monitor and troubleshoot their systems, leading to better performance, reliability, and customer satisfaction.

Why observability is important in cloud based environment

Observability is the ability to gain insight into the internal state of a system by observing its external outputs. In modern cloud environments, observability is crucial for identifying and resolving issues quickly and efficiently. It involves collecting, analyzing, and visualizing data from various sources, such as logs, metrics, and traces, to gain a holistic view of the system's health and performance. Observability is achieved through the implementation of various tools and techniques, including automated monitoring, real-time topology mapping, and AI-based anomaly detection. By leveraging these technologies, teams can proactively identify and remediate issues before they impact end-users, ensuring high levels of system reliability and availability.

Challenges at scale

Achieving observability at scale in modern cloud environments can be challenging due to various reasons. One of the most common challenges is dealing with data overload where organizations collect an enormous amount of data from different sources, making it difficult to identify and interpret the critical information. Additionally, siloed teams also pose a challenge, as different teams have different tools and data sources, which can lead to a lack of shared context, limited visibility, and incomplete information. It can be challenging to achieve a unified view of the entire system, leading to difficulties in diagnosing and resolving issues. Addressing these challenges requires the use of advanced observability techniques and tools that can automate data collection, aggregation, and analysis across the entire stack, providing a unified view of the system.

The role of automation in observability:

In observability, automation plays a crucial role in collecting and analyzing data without human intervention, helping organizations handle the growing amounts of data generated by modern cloud environments. Automation eliminates the need for manual monitoring, reducing the risk of errors and saving time. It can help collect data from various sources and integrate it into a single platform, making it easier for IT teams to analyze data in real-time. Automated alerting can also detect issues and notify teams immediately, enabling them to respond promptly to resolve incidents. Additionally, automation can enable the creation of dynamic dashboards and reports to provide a comprehensive view of the system's health and performance.

The benefits of automation:

Automating observability processes can bring significant benefits in terms of scalability and completeness. With the ability to automatically collect and analyze large volumes of data, automation can enable organizations to monitor their cloud environments more comprehensively and at a much larger scale. This can help to identify issues and trends more quickly, improving incident response times and reducing the risk of service disruptions. Automation can also enable teams to identify and address issues before they impact end-users, helping to improve the overall user experience. Additionally, automation can help to break down silos between teams by providing a shared, standardized view of data, reducing the risk of miscommunication and errors.

How AI can enhance observability:

AI can enhance observability by analyzing large volumes of data generated by cloud applications and infrastructure. For example, AI algorithms can detect patterns and anomalies in log files, network traffic, and application performance metrics that would be difficult for humans to identify.

For instance, if a particular type of error is occurring frequently, AI can identify the underlying issue and provide actionable insights to resolve it. AI can also analyze data from multiple sources to identify potential issues across the entire infrastructure, enabling teams to take proactive measures to prevent downtime or other problems.

Another example is in the case of security observability. AI can detect and alert teams to potential security threats by analyzing network traffic patterns, login attempts, and user behavior, and can also perform automated remediation actions to prevent further damage.

Examples of companies using observability with automation and AI :

  • Netflix: One example of a company using observability with automation and AI is Netflix. They use a platform called Atlas, which provides real-time monitoring and analytics of their distributed systems. Atlas uses automation to collect and analyze large amounts of data from different sources, including logs, metrics, and traces. AI algorithms are used to identify patterns and anomalies in the data, which helps Netflix to quickly detect and resolve issues before they impact customers.

  • Shopify: Another example is Shopify, an e-commerce company that uses observability with automation and AI to improve the performance and reliability of their platform. They use a tool called Skylight, which provides real-time visibility into their Ruby on Rails applications. Skylight uses automation to collect and analyze data from different sources, including code-level profiling and server metrics. AI algorithms are used to identify performance bottlenecks and suggest optimizations, which helps Shopify to improve the customer experience.

  • Capital One: A third example is Capital One, a financial services company that uses observability with automation and AI to enhance security and compliance. They use a platform called Cloud Custodian, which provides automated policy enforcement across their cloud infrastructure. Cloud Custodian uses automation to monitor and enforce security policies, such as encryption and access controls. AI algorithms are used to detect anomalies in user behavior and network traffic, which helps Capital One to identify and mitigate security risks.

As technology continues to evolve, the field of observability is also expected to change in the future. Here are some of the potential trends that might shape the future of observability:

  1. Greater adoption of machine learning and AI: As companies continue to grapple with massive amounts of data, they will likely rely more heavily on machine learning and AI algorithms to help identify patterns and anomalies in their systems.

  2. More emphasis on end-to-end visibility: In the past, observability was often focused on specific parts of the system. In the future, however, there will likely be a greater emphasis on end-to-end visibility, which will help teams identify and address issues that span multiple parts of the system.

  3. Increased use of automation: Automation will likely continue to play a big role in observability, helping teams collect, analyze, and act on data more quickly and efficiently.

  4. Greater integration with DevOps: As observability becomes more important to modern cloud environments, it will likely become even more closely integrated with DevOps practices, allowing teams to more easily identify and fix issues as they arise.

  5. Adoption of new technologies: As new technologies emerge, such as serverless computing and containerization, the field of observability will need to evolve to accommodate these new architectures and provide visibility into these systems.


The blog post "Observability 2.0” explores the role of artificial intelligence and automation in observability, which refers to the ability to gain insights into the behavior and performance of complex systems. The article discusses the challenges of traditional monitoring and how AI and automation can help overcome those challenges, providing more accurate and real-time insights into system performance. The post also explores different tools and techniques that are available to help organizations implement observability with AI and automation, and highlights some best practices for ensuring that these systems are effective and efficient.

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