Wild Moose

Wild Moose is an AI site reliability engineering product that acts as a first responder when production alerts fire. Instead of a single opaque model, it launches a set of specialized AI micro agents that run checks in parallel across the tools a team already uses, gather evidence, rank hypotheses and return a structured root cause analysis with suggested next steps, such as rolling back a specific deploy. It is aimed at engineering and operations teams running complex or fast moving production environments who want to reduce on call burden. The agents are managed as code. When an investigation exposes a gap, the fix is committed as a pull request and tested against past incidents before it runs in production, so the investigation logic can be audited and improved. The platform also looks across repeated investigations to surface recurring causes, noisy alerts, missing telemetry and ownership gaps, with the stated aim of preventing incidents rather than only resolving them faster. It reads alerts, logs, traces, deploys, tickets, metrics and runbooks, and connects to tools including Confluence, Jira, GitLab and Kubernetes. The vendor says it does not require data cleanup before use. The vendor does not publish prices. The pricing page is a contact page, and access starts with booking a demo or call with the team. Wild Moose was founded by Yasmin Dunsky, Roei Schuster and Tom Tytunovich, and describes its goal as AI becoming the first responder in incident response.