Observability solutions are becoming increasingly important in today’s digital age. As more and more companies rely on technology to run their business operations, the need to monitor, analyse, and optimise their systems and applications in real-time has become critical. Organisations are now prioritising customer experience and accessibility through digital transformation.

However, these initiatives can introduce complexity and challenges for engineers. “Efficient observability solutions are crucial for businesses scaling their digital outreach, as they face increasing device diversity. 

“Engineers require end-to-end observability to untangle data, understand business performance, monitor service availability, and optimise further. New Relic serves as a comprehensive observability solution, offering real-time insights and empowering engineers to make data-driven decisions throughout the software lifecycle,” Ganesh Narasimhadevara, Principal Technologist, APJ, New Relic told AIM.

New Relic’s comprehensive platform offers over 30 capabilities, delivering a seamless and connected experience throughout the various layers of the technology stack at every stage of the software lifecycle. “We started our journey with the very first capability of application performance monitoring and then we extended ourselves along with the tech advancements that we’re seeing in the market. We extended into infrastructure monitoring, not limited to the VMs or just to the on-prem servers. As of today, we have the most advanced Kubernetes monitoring in the industry.”

New Relic Grok

But today, we are in the age of generative AI and it is finding use cases in the observability solutions space as well. New Relic, a US-based Observability platform announced New Relic Grok, the world’s first generative AI assistant for observability. 

Narasimhadevara, in an exclusive conversation with AIM, said that New Relic’s generative AI powered observability is what engineers would need to be more productive every day. “New Relic Grok empowers users to identify instrumentation gaps and provides guidance for onboarding new services. It can sift through extensive documentation to answer questions and generate queries for specific metrics or application trends. Additionally, it facilitates common use cases such as creating alerts or service levels.”

New Relic is leveraging generative AI capabilities through Microsoft OpenAI Azure services (GPT models) and is not exploring building its own proprietary models. However, it is something that New Relic could look into in the future.

“Currently, I don’t see the immediate necessity for building our own language models. Many organisations, including those in India, are partnering with hyperscalers to leverage their existing efficient models. The focus is on understanding how generative AI can enhance observability before investing resources in model development. Building our own model may be a future plan once we have assessed its benefits and potential.”

How gen AI can benefit observability solutions 

Today, companies in the observability space are developing new generative AI solutions that can help automate monitoring, visualisation, and analysis of complex systems, reducing manual effort, speeding up issue identification and resolution, and improving overall system performance. 

In short, generative AI is making it easier for users to find the root cause of an issue and fix the errors. “Observability solutions have evolved to be a single source of truth. Having a generative AI system as part of the product is going to eliminate the need of jumping between a tool like ChatGPT, and observability solutions,” Narasimhadevara said.

The advantages that generative AI brings to observability solutions will help in reducing the workload of engineers and help save a lot of time allowing them to focus on more strategic work. “Another way generative AI solutions with observability can evolve is by bypassing the tribal knowledge, according to Narasimhadevara. Tribal knowledge refers to experience and knowledge that is shared among team members but is not documented or shared more widely across the organisation.

“Team members may vary in their expertise and product knowledge. Imagine having the capability to analyse vast amounts of data, process tribal knowledge, detect anomalies, and connect various metrics to provide insights such as identifying the cause of an anomaly due to a recent deployment. Generative AI solutions integrated effectively can offer such capabilities, providing relevant suggestions and allowing users to train the model to meet their specific needs.”

Moreover, another good advancement in observability that could emerge from generative AI is automation. Going forward, we could foresee a scenario where a Large Language Model would be able to perform a task without any human intervention. “In future engineers will have the ability to instruct the generative AI assistant to monitor the application performance, provide some recommendations and also implement them through automation.”

What impact could GenAI have on observability solution providers?

Narasimhadevara believes there are similarities between observability solutions and large language models. “The reason why it’s very similar is that they get as good as the data that you feed them.”

Hence, observability solution providers only stand to benefit from generative AI. The effectiveness of language models is contingent on the data they have access to. Observability solutions, on the other hand, provide critical insights into system states that are exclusive to their functionality. “Integrating observability solutions with advanced or current language models can enhance contextual understanding. Without the relevant data from observability solutions, the language model’s power is limited.”