Honeycomb today added a Query Assistant to its observability platform that uses OpenAI’s ChatGPT generative artificial intelligence (AI) platform to launch queries via a natural language interface rather than having to master a query language.
That capability complements an existing tool based on machine learning algorithms, dubbed BubbleUP, that DevOps teams already use to debug code.
Honeycomb CTO Charity Majors said both tools make the Honeycomb observability platform more accessible to IT teams that are tasked with managing complex application environments. Those teams are not going to be able to accomplish that goal without relying more heavily on AI to determine the root causes of an issue.
Generative AI augments DevOps teams by making it possible to build a relevant, modifiable query that they can continuously iterate as IT staff investigate an issue. This approach means teams don’t necessarily need a deep understanding of code behavior and the underlying infrastructure it depends on, noted Majors.
That’s critical, because not every IT professional immediately knows what query to launch. One of the challenges with adopting any observability platform is they require a a DevOps team member to frame a query to generate a result. If multiple queries are required, coding them in a query language becomes a cumbersome task.
As AI continues to improve, algorithms should be able to automatically surface more issues. But given all the dependencies that exist in modern application environments, there will still be a need for a DevOps specialist to ensure application availability and optimize performance, at least for the foreseeable future.
In general, generative AI represents a significant leap forward compared to AI for IT operations (AIOps) platforms that, in comparison, are not nearly as useful, noted Majors.
It’s still early days as far as measuring the impact AI will have on DevOps workflows. But the AI genie is already out of the proverbial bottle. Many of the low-level tasks that tend to make DevOps jobs tedious will soon be automated. As a result, job roles within DevOps teams will need to evolve.
Less clear is what impact the rise of generative AI might have on the adoption of observability platforms. While there is no shortage of observability platforms, adoption has been limited by the number of DevOps professionals that could master a specific query language created for that observability platform. The ability to rely on a natural language interface instead reduces the need to use a proprietary query tool.
In the longer term, the simpler it becomes to use DevOps best practices, the more widely they will be adopted. AI platforms should enable more organizations to embrace DevOps in a way that reduces the level of cognitive load currently required.
In the meantime, there’s no doubt that current DevOps professionals are cautiously watching the rise of generative AI, much like everyone else. The difference is that most DevOps professionals are not going to want to work for organizations that don’t provide access to AI-infused tools and platforms that make their jobs easier.
