Causeloom is an AI investigator for production incidents. A lead AI agent and its subagents read your logs, trace the code, and recall past tickets and docs, then hand you a root cause with the evidence attached.
Row by row, the findings lock together into one piece of cloth: the root cause.
Each subagent takes one source and reports back what it found. The lead agent weighs the findings and writes the conclusion.
This case is an illustrative example, not customer data. Press the button to replay it.
Point Causeloom at your logs, your repositories, and your knowledge base in Jira and Confluence.
A lead AI agent plans the investigation and sends specialised subagents into each source at the same time.
The AI writes an analysis that names the cause and cites the log lines, code, and past tickets behind it.
Searches and correlates log lines around the incident window.
Reads the code paths involved to explain why the failure happened.
Finds past incidents that look like this one and how they were resolved.
Pulls in runbooks, design docs, and postmortems as context.
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Each conclusion points back to the log lines, code, and tickets it rests on.
Past tickets and postmortems are checked on every case, so known failure patterns are recognised.
Subagents work separate sources and their findings are woven into a single analysis.
See the AI agents investigate a real case from your own stack.