What platforms does Wild Moose support?

Wild Moose is available on Web App.

What are the limitations of Wild Moose?

No published pricing, plans or free tier, so evaluation requires a sales conversation. Homepage makes no claims about compliance certifications, data residency or where telemetry is processed. Value depends on having enough historical incidents and signal volume for the agents to learn from.

Wild Moose

Name: Wild Moose
Author: Wild Moose

AI SRE agents that investigate alerts and find root cause

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Screenshot of Wild Moose, AI SRE agents that investigate alerts and find root cause

What is Wild Moose?

Wild Moose is an AI task generator tool. Wild Moose runs a fleet of AI SRE agents that investigate production alerts, cite the evidence behind a root cause, and propose a fix. It also mines recurring investigation history to reduce alert noise and prevent repeat incidents.

Wild Moose is an AI SRE platform built around a fleet of agents that investigate production alerts on their own. Instead of paging an engineer to sift through dashboards at 3am, the product reads the environment a company already runs on (logs, traces, metrics, deploy history, tickets, runbooks, chat threads) and produces a diagnosis with the evidence attached and a suggested next step, such as rolling back a specific pull request. The homepage shows one such investigation resolving in 35 seconds with seven checks cited. The design goal is not faster firefighting but fewer fires. Wild Moose deploys small, purpose-built agents, each scoped to a signal class: p99 latency by service, database connection pool saturation, past incident history, 5xx rates by endpoint, heap and memory pressure. Agents observe everything down to low-severity noise. When an engineer corrects a diagnosis or a runbook changes, the system replays the new evidence against earlier investigations, back-tests it, and publishes the improvement as a versioned change with a regression run, so tuning accumulates rather than resetting. The second layer is pattern analysis. Across repeated investigations the platform surfaces recurring causes, alert noise, missing telemetry and ownership gaps, then proposes concrete fixes such as widening an evaluation window on a gRPC deadline, with an estimated reduction in weekly alerts. It is aimed at engineering and SRE teams in high-traffic environments, and the reference stories (Wix, Redis, GoFundMe) suggest companies with thousands of services and large on-call loads. Setup is described as requiring no telemetry cleanup, which matters for organisations with messy or half-documented systems. Compared with observability suites that add another dashboard, Wild Moose positions itself as an agent layer on top of existing monitoring, ticketing and code tooling. Pricing is not published, and the entry point is a scheduled demo.

How do you use Wild Moose?

1Book a demo with the Wild Moose team
Access starts through a scheduled 30 minute demo. Use it to describe your alert volume, service count and current tooling so the team can scope agents for your environment.
2Connect your observability and incident sources
Wire the platform to your existing monitoring and alerting systems: Datadog, Grafana, Prometheus, CloudWatch, PagerDuty, Opsgenie and Slack threads are the sources shown on the homepage.
3Add code, ticketing and runbook context
Link GitHub or GitLab for deploys and pull requests, Jira for incidents and tickets, Confluence for runbooks, and Kubernetes for workload events. This context is what lets agents name a specific cause.
4Review the agent fleet assigned to your stack
The team builds agents around the signals you actually emit, such as latency, database saturation, error rates and memory. Each agent is scoped to a signal class and a service.
5Read your first investigations and correct them
Open the investigation summaries, check the evidence list against the proposed cause, and correct anything that is wrong. Corrections feed the tuning loop.
6Track tuning and noise reduction over time
Watch agent versions update as new evidence is back-tested and published, and review the periodic pattern reports for recurring causes and alert noise.

Pros and cons

Pros

Investigations come with cited evidence and a concrete next step, not just a suspicionAI
Agents are scoped per signal (latency, DB pools, memory, error rates), which keeps diagnoses narrow and checkableAI
Corrections from engineers are back-tested against past incidents and shipped as versioned agent updatesAI
Case studies from Wix, Redis and GoFundMe report measurable MTTR and root cause accuracy gainsAI
Tolerates existing telemetry and runbooks without a cleanup project firstAI

Cons

No published pricing, plans or free tier, so evaluation requires a sales conversationAI
Homepage makes no claims about compliance certifications, data residency or where telemetry is processedAI
Value depends on having enough historical incidents and signal volume for the agents to learn fromAI
Fits the incident-response stack only; not a replacement for dashboards, on-call routing or logging storageAI

How much does Wild Moose cost?

Integrations

The homepage lists Confluence, Jira, GitLab, Kubernetes, Opsgenie, Grafana, Slack, GitHub, CloudWatch, Prometheus, PagerDuty and Datadog as connected sources for runbooks, incidents, code, workloads, alerts, threads, deploy history and metrics.

Features

A fleet of scoped AI SRE agents investigate alerts across logs, traces, metrics, deploys, tickets, threads and runbooks, returning a root cause, an evidence list and a suggested next step. Engineer corrections, new runbooks and new signals are replayed against past investigations, regression tested and published as versioned agent updates. Pattern analysis across investigations surfaces recurring causes, noisy alerts, missing telemetry and ownership gaps.

Frequently asked questions about Wild Moose

How do you use Wild Moose?
The walkthrough on this page covers 6 steps: 1. Book a demo with the Wild Moose team 2. Connect your observability and incident sources 3. Add code, ticketing and runbook context 4. Review the agent fleet assigned to your stack 5. Read your first investigations and correct them 6. Track tuning and noise reduction over time.
What platforms does Wild Moose support?
Wild Moose is available on Web App.
What does Wild Moose integrate with?
The homepage lists Confluence, Jira, GitLab, Kubernetes, Opsgenie, Grafana, Slack, GitHub, CloudWatch, Prometheus, PagerDuty and Datadog as connected sources for runbooks, incidents, code, workloads, alerts, threads, deploy history and metrics.
What are the limitations of Wild Moose?
No published pricing, plans or free tier, so evaluation requires a sales conversation. Homepage makes no claims about compliance certifications, data residency or where telemetry is processed. Value depends on having enough historical incidents and signal volume for the agents to learn from.