IT operations people have enough going on in their plates, and when an incident arrives that cripples a major system, time will always work against them. Over time, companies have looked for an edge in rising faster with playbooks intended to identify answers to common problems and postmortems 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 actually forms a perfect problem for generative AI to solve, and AIOps startup BigPanda today announced a new tool, called Biggy, to help some of these be figured out quicker. Biggy is an intent-based tool that will look across a wide variety of IT-related data, learn how the company operates, and compare it to the problem scenario and other similar scenarios to suggest a solution.

BigPanda was a company that used AI since its very early days, and it deliberately designed two separate systems: the one for the data layer and the other for the AI. This way, it prepared itself for this shift toward generative AI 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 Biggy, and what we're doing with generative and conversational AI," BigPanda CEO Assaf Resnick told TechCrunch.

This tool, like most generative AI tools, 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 about a specific 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 particular 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 is a lot of technical jargon, but in summary, it looks at system-level information, organizational data and human interactions that deliver responses to help engineers solve the problem.

When a user input's a prompt, it searches through all the data to give its best shot at an answer that will hopefully point the engineers in the right direction to fix the problem. They acknowledge not always being perfect, for no generative AI is ever going to be, but they let the user know when they have a lesser degree of certainty that the answer is correct.

"For areas where we think we 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 is unlikely to be trivially simple for customers, and that's a step in the right direction for building a usable AI assistant that helps IT get at root causes of problems and solve them faster. No one wants the claims made by some AI evangelists about their technology being "unbreakable." But an interactive AI tool might be better than the more tedious approaches most of us currently use to troubleshoot our IT systems.

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