IT operations personnel have a lot going on, and when an incident occurs that brings down a key system, time is always going to be against them. Over the years companies have looked for an edge in getting up faster with playbooks designed to find answers to common problems, and post mortems to keep them from repeating, but not every problem is easily solved, and there is so much data and so many possible points of failure.
It’s actually a perfect problem for generative AI to solve, and AIOps startup BigPanda, announced a new generative AI tool today called Biggie to help solve some of these issues faster. What Biggie has been designed to do is to look across a vast variety of IT-related data to learn how the company operates and compare it to the problem scenario and other similar scenarios and suggest a solution.
BigPanda has been using AI since the early days of the company, and deliberately designed two separate systems: one for the data layer and another for the AI, and this in a way prepared them for this shift to generative AI based on large language models. “The AI engine before Gen AI was building a lot of other types of AI, but it was feeding off of the same data engine that will be feeding what we’re doing with Biggie, and what we’re doing with generative and conversational AI,” BigPanda CEO Assaf Resnick told TechCrunch.
Like most generative AI tools, this one makes a prompt box available where users can ask questions and interact with the bot. In this case, the underlying models have been trained on data inside the customer company, as well as on publicly available data on a particular piece of hardware or software, and are tuned to deal with the kinds of problems IT deals with on a regular basis.
“The out-of-the box LLMs have been trained on a huge amount of data, and they’re really good actually as generalists in all of the operational fields we look at – infrastructure, network, application development, everything there. And they actually know all the hardware very well,” Jason Walker, chief innovation officer at BigPanda said. “So if you ask it about a certain HP blade server with this error code, it’s pretty good at putting that together, and we use that for a lot of the event traffic.” Of course it has to be more than that or a human engineer could simply look this up in Google Search.
It combines this knowledge with what it is able to cull internally across a range of data types. “BigPanda ingests the customer’s operational and contextual data from observability, change, CDMB (the file that stores configuration information) and topology along with historical data and human, institutional context — and normalizes the data into key-value pairs, or tags,” Walker said. That’s a lot of technical jargon, but basically it means it looks at system-level information, organizational data and human interactions to deliver a response to help engineers solve the problem.
When a user enters a prompt, it looks across all the data to generate an answer that will hopefully point the engineers in the right direction to fix the problem. They acknowledge that it’s not always perfect because no generative AI is, but they let the user know when there is a lower degree of certainty that the answer is correct.
“For areas where we think we not don’t have as much certainty, then we tell them that this is our best information, but a human should take a look at this,” Resnick said. For other areas where there is more certainty, they may introduce automation, working with a tool like Red Hat Ansible to solve the issue without human interaction, he said.
The data ingestion part isn’t always going to be trivial for customers, and this is a first step toward providing an AI assistant that can help IT get at the root of problems, and solve them faster. No AI is foolproof, but having an interactive AI tool should be an improvement over current more time-consuming manual approaches to IT systems troubleshooting.
