Reposted with permission from LinkedIn.
Modernization Of The RCA Process
Over the past month, I have spent a significant amount of time researching what vendors and customers are doing in the devops space to streamline the process of root cause analysis (RCA).
My conclusion is that the underlying techniques and processes used in operational environments today to perform RCA remain human centric. As a consequence troubleshooting remains complex, resource intensive and requires skilled practitioners to perform the work.
So, how do we break free from this human bottleneck? Brace yourselves for a glimpse into a future powered by AI. In this article, we’ll dissect the critical issues, showcase how cutting-edge AI advancements can revolutionize RCA, and hear first hand from operations and engineering leaders who have shared their perspective on this transformative tech, having experienced the capabilities first hand.
Troubleshooting In The Cloud Native Era With Monitoring & Observability
Troubleshooting is hard because when degradations or failures occur in components of a business service, they spread like a disease to related service entities which also become degraded or fail.
This problem is amplified in the world of cloud-native applications where we have decomposed business logic into many separate but interrelated service entities. Today an organization might have hundreds or thousands of interrelated service entities (micro services, databases, caches, messaging…).
To complicate things even further, change is a constant – code changes, fluctuating demand patterns, and the inherent unpredictability of user behavior. These changes can result in service degradations or failures.
Testing for all possible permutations in this ever-shifting environment is akin to predicting the weather on Jupiter – an impossible feat – amplifying the importance of a fast, effective and consistent root cause analysis process, to maintain the availability, performance and operational resilience of business systems.
While observability tools have made strides in data visualization and correlation, their inherent inability to explain the cause-and-effect relationships behind problems leaves us dependent on human expertise to navigate the vast seas of data to determine the root cause of service degradation and failures.
This dependence becomes particularly challenging due to siloed devops teams that have responsibility for supporting individual service entities within the complex web of services entities that make up business services. In this context individual teams may frequently struggle to pinpoint the source of service degradation or failure as the entity they support might be the culprit, or a victim of another service entity’s malfunction.
The availability of knowledge and skills within these teams also fluctuate due to business priorities, vacations, holidays, and even the daily working cycles. This resource variability can lead to significant inconsistencies in problem identification and resolution times.
Causal AI To The Rescue: Automating The Root Cause Analysis Process For Cloud Native DevOps
For those who are not aware, Causal AI is a distinct field in Artificial Intelligence. It is already used extensively in many different industries but until recently there has been no application of the technology in the world of devops.
Causely is a new pioneer championing the use of Causal AI working in the area of cloud-native applications. Their platform embodies an understanding of causality so that when service entities are degraded or failing and affecting other service entities that make up business services, it can explain the cause and effect, by showing the relationship between the problem and the symptoms that this causes.
Through this capability, the team with responsibility for the failing or degraded service can be immediately notified and get to work on resolving the problem. Other teams might also be provided with notifications to let them know that their services are affected, along with an explanation for why this occurred. This eliminates the need for complex triage processes that would otherwise involve multiple teams and managers to orchestrate the process.
Understanding the cause-and-effect relationships in software systems serves as an enabler for automated remediation, predictive maintenance, and planning/gaming out operational resilience.
By using software in this way to automate the process of root cause analysis, organizations can reduce the time and effort and increase the consistency in the troubleshooting process, all of which leads to lower operational costs, improved service availability and less business disruption.
Customer Reactions: Unveiling the Transformative Impact of Causal AI for Cloud-Native DevOps
After sharing insights into Causely’s groundbreaking approach to root cause analysis (RCA) with operational and engineering leaders across various organizations, I’ve gathered a collection of anecdotes that highlight the profound impact this technology is poised to have in the world of cloud-native devops.
Streamlined Incident Resolution and Reduced Triage
“By accurately pinpointing the root cause, we can immediately engage the teams directly responsible for the issue, eliminating the need for war rooms and time-consuming triage processes. This ability to swiftly identify the source of problems and involve the appropriate teams will significantly reduce the time to resolution, minimizing downtime and its associated business impacts.”
Automated Remediation: A Path to Efficiency
“Initially, we’d probably implement a ‘fix it’ button that triggers remediation actions manually. However, as we gain confidence in the results, we can gradually automate the remediation process. This phased approach ensures that we can seamlessly integrate Causely into our existing workflows while gradually transitioning towards a more automated and efficient remediation strategy.”
Empowering Lower-Skilled Team Members
“Lower-skilled team members can take on more responsibilities, freeing up our top experts to focus on code development. By automating RCA tasks and providing clear guidance for remediation, Causely will empower less experienced team members to handle a wider range of issues, allowing senior experts to dedicate their time to more strategic initiatives.”
Building Resilience through Reduced Human Dependency
“Causely will enable us to build greater resilience into our service assurance processes by reducing our reliance on human knowledge and intuition. By automating RCA and providing data-driven insights, Causely will help us build a more resilient infrastructure that is less susceptible to human error and fluctuations in expertise.”
Enhanced Support Beyond Office Hours
“We face challenges maintaining consistent support outside of office hours due to reduced on-call expertise. Causely will enable us to handle incidents with the same level of precision and efficiency regardless of the time of day. Causely’s ability to provide automated RCA and remediation even during off-hours ensures that organizations can maintain a high level of service continuity around the clock.”
Automated Runbook Creation and Maintenance
“I was planning to create runbooks to guide other devops team members through troubleshooting processes. Causely can automatically generate and maintain these runbooks for me. This automated runbook generation eliminates the manual effort required to create and maintain comprehensive troubleshooting guides, ensuring that teams have easy access to the necessary information when resolving issues.”
Simplified Post-Incident Analysis
“Post-incident analysis will become much simpler as we’ll have a detailed record of the cause and effect for every incident. Causely’s comprehensive understanding of cause and effect provides a valuable resource for post-incident analysis, enabling us to improve processes, and prevent similar issues from recurring.”
Faster Problem Identification and Reduced Business Impacts
“Problems will be identified much faster, and there will be fewer business consequences. By automating RCA and providing actionable insights, Causely can significantly reduce the time it takes to identify and resolve problems, minimizing their impact on business operations and customer experience.”
These anecdotes underscore the transformative potential of Causely, offering a compelling vision of how root cause analysis is automated, remediation is streamlined, and operational resilience in cloud-native environments is enhanced. As Causely progresses, the company’s impact on the IT industry is poised to be profound and far-reaching.
Summing Things Up
Troubleshooting in cloud-native environments is complex and resource-intensive, but Causal AI can automate the process, streamline remediation, and enhance operational resilience.
If you would like to learn more about how Causal AI might benefit your organization, don’t hesitate to reach out to me or Causely directly.
Related Resources
- Learn about the causal AI platform from Causely
- Watch the video: Troubleshooting cloud-native applications with Causely
- Request a demo to see Causely in action