Chronosphere, the observability platform built for control, announced the launch of AI-guided troubleshooting capabilities, a major advancement that redefines how engineering teams investigate and resolve production incidents.
The new set of capabilities combines AI-driven insights with deep environmental context via a temporal knowledge graph. With this context, Chronosphere delivers highly accurate root-cause insights that enable engineers to resolve issues faster and with greater confidence.
Advancements in software development
Research from MIT and the University of Pennsylvania found that generative AI spurred a 13.5 percent increase in weekly code commits, signifying a surge in code velocity and change volume.
Despite these advancements in software development, troubleshooting remains primarily manual and relies heavily on intuition, resulting in slower mean time to resolution (MTTR) and greater on-call stress.
Chronosphere's AI-guided troubleshooting capabilities
Chronosphere's AI-guided troubleshooting capabilities close this gap by combining AI reasoning with a temporal knowledge graph – a living, queryable map of an organisation's services, infrastructure, and their relationships. It accounts for system changes and even human input.
Unlike observability tools that run on proprietary or standard data inputs, it also integrates custom application telemetry, providing the deep context needed for effective root-cause analysis.
Chronosphere's advanced analytics
With this context in place, the system then applies Chronosphere's advanced analytics to surface the most meaningful next steps in an investigation.
At each stage, it explains what's been analysed or ruled out, allowing 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.
Data foundation and analytical depth
"For AI to be effective in observability, it needs more than pattern recognition and summarisation," said Martin Mao, CEO and co-founder of Chronosphere.
"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."
Four core capabilities
Chronosphere's AI-guided troubleshooting introduces four core capabilities:
- Suggestions: Proactive, plain-language insights that guide investigations toward likely causes – backed by data, not guesswork.
- Temporal knowledge graph: A continuously updated map of services, dependencies, and custom telemetry, capturing full system context.
- Investigation notebooks: Persistent workspaces that document every step, piece of evidence, and conclusion, turning investigations into reusable institutional knowledge.
- Natural language assistance: Engineers can now build queries and dashboards using natural language, accelerating data exploration.
Availability of the MCP Server
In addition to AI-guided troubleshooting, Chronosphere announced the general availability of its Model Context Protocol (MCP) Server, enabling engineers and developers to integrate Chronosphere directly into internal AI workflows.
This level of deeper integration empowers teams to leverage large language models (LLMs) and securely query observability data through familiar tools such as Codex, PromptIDE, or other AI-enabled IDEs.
AI-guided troubleshooting, including suggestions and investigation notebooks, is in limited availability now, with full general availability planned for 2026. MCP integration is available now for all Chronosphere customers.