Cloud-native observability company Chronosphere Inc. today announced the launch of AI-Guided Troubleshooting capabilities, an advancement that helps engineering teams investigate and resolve production incidents.

The new capabilities combine artificial intelligence-driven insights with deep environmental context via a Temporal Knowledge Graph to deliver highly accurate root-cause insights that enable engineers to resolve issues faster and with greater confidence.

The company’s new AI-Guided Troubleshooting seeks to assist with the issue whereby generative AI has spurred a 13.5% increase in weekly code commits, signifying a surge in code velocity and change volume. Chronosphere argues that despite these advancements in software development, troubleshooting remains primarily manual and relies heavily on intuition, resulting in slower mean time to resolution and greater on-call stress.

Chronosphere’s AI-Guided Troubleshooting capabilities close the gap by combining AI reasoning with a Temporal Knowledge Graph, a living, queryable map of an organization’s services, infrastructure and their relationships. The capabilities account for system changes and even human input, as well as supporting custom application telemetry to provide deep context for effective root-cause analysis.

The system applies analytics to surface the most meaningful next steps in an investigation. Each stage includes explanations of what’s been analyzed or ruled out to allow engineers to stay in control while AI accelerates every phase of the troubleshooting process. As engineers zero in on a root cause, investigations are fed into the Temporal Knowledge Graph so future suggestions get smarter.

“For AI to be effective in observability, it needs more than pattern recognition and summarization,” said co-founder and Chief Executive Martin Mao. “Chronosphere has spent years building the data foundation and analytical depth needed for AI to actually help engineers. With our Temporal Knowledge Graph and advanced analytics capabilities, we’re giving AI the understanding it needs to make observability truly intelligent and giving engineers the confidence to trust its guidance.”

Chronosphere’s AI-Guided Troubleshooting introduces four core capabilities. Suggestions offer proactive, plain-language insights that guide investigations toward likely causes backed by data, not guesswork. The Temporal Knowledge Graph gives a continuously updated map of services, dependencies and custom telemetry. Investigation Notebooks provides persistent workspaces that document every step, piece of evidence and conclusion, turning investigations into reusable institutional knowledge. And Natural Language Assistance allows engineers to build queries and dashboards using natural language, accelerating data exploration.

In addition to AI-Guided Troubleshooting, Chronosphere also today announced the general availability of its Model Context Protocol Server which allows engineers and developers to integrate Chronosphere directly into internal AI workflows.

Chronosphere CEO Mao spoke with theCUBE, SiliconANGLE Media’s livestreaming studio, in June, when he discussed how Chronosphere is redefining cloud-native observability with Logs 2.0 and real-time data control:

Image: Chronosphere