1Late detection
Critical issues are often discovered by users first
Many teams don't have alerting that catches every production issue. Customer reports become the first signal - and by then, trust is already slipping.
AI SRE Agent·Autonomous Issue Detection & Root Cause Analysis
AI-Powered Root Cause Analysis
in seconds.
Metoro detects incidents from live traffic, investigates autonomously across code, infra, logs, and traces, and hands you a review-ready fix PR. No alerts to tune. No dashboards to search.
Free trial · No credit card · Deploy in under a minute
Trusted by hundreds of the best at
Porter
Remy Security
Porter
Remy Security
DocioHealth
Porter
The Problem
Incident response still starts too late.
Teams learn about failures after user impact, then spend hours isolating signal from noise.
2Slow setup cycle
Good alerting takes multiple incidents to mature
Reliable alerts aren't instant. They're tuned over time, after pages, misses, and repeated postmortems. You pay for every iteration.
3Investigation overload
Every anomaly still requires manual triage
Most anomalies aren't severe, but engineers still spend time investigating each one to find the real problems hiding in the noise.
The Solution
Cut MTTR with autonomous investigation.
Always-on issue detection. Root cause analysis that runs itself. A pull request with the fix, ready for review - usually before the first engineer joins the war room.
01Detection
activebooking-svc · p95anomaly
11:0411:1211:20
Detect real incidents automatically.
Metoro spots behavior regressions from live cluster signals without predefined alerts. Anomalies get scored, noise stays in noise.
02Root cause
correlation chain
telemetry500 spike · 22.8%
deploycommit 9a7e2c3
root causestale auth cache
Find the exact failure chain.
The AI correlates telemetry, deploy events, and code context to isolate why the incident happened - not just where.
03Remediation
PR #487open
booking_service.go
− if err != nil { return err }
+ profile, err = s.retryWithRefresh(ctx)
ci · 14/14lint clean
Open a fix PR, not just an alert.
Metoro proposes a targeted patch and raises a pull request with evidence your team can review, edit, and merge.
How it works
Root cause quality depends on context quality.
Metoro combines layered runtime and engineering context so investigation quality improves from signal to signal, not guess to guess.
1
eBPF kernel signal stream
Reads signals directly from the Linux kernel - per-call traces, logs, metrics - with no SDK, no sidecar, no instrumentation work.
2
Custom OTel metrics and traces
Bring domain-specific signals through OTLP or Prometheus remote write endpoints to enrich the model.
3
Code repositories and deploy history
Connect runtime failures to commits, owners, and recently shipped changes - so the AI can reason across the whole system.
4
Past incidents and runbooks
Use incident memory to recognize recurring patterns and move to remediation faster.
Outcome
RCA that actually works.
<5m
Typical time to root cause
vs. 30m+ industry average
100%
Request + event coverage
no sampling, kernel-level eBPF
0
Alerts to configure
anomaly detection by default
1m
To deploy Metoro
single Helm install
Customer feedback
What teams are saying.
Metoro has made visibility into our Kubernetes environment effortless with on-demand event analysis and AI-driven root-cause investigations. Nothing is hidden anymore.
Metoro absolutely slaps, so good ❤️
Detection, investigation, and the fix PR - all before I finished reading the page. It's the first AI SRE that's actually earned its name.
Metoro has been a huge boon to our observability ecosystem; saving us time and effort getting the information we care about most out of our clusters. The only thing cooler than the tool has been the people behind it.
It found exactly what I was looking for in the logs. Amazing.
We used to spend an hour digging through dashboards when something broke. Now Metoro figures it out in minutes - our on-call engineers love it.
AI root cause analysis is just amazing. Helps us save a ton of time.
We installed Metoro, and it just worked.
I'm literally able to look up at a Slack notification from Metoro whilst having noodles, tap the link, access the Metoro dashboard, see what customers on Porter Cloud are doing and take a call in real-time. For me, that's the best thing ever.
In the last week, we've detected and blocked 10 malicious agents running on our infrastructure. Without Metoro, they would still likely be running.
Metoro made it incredibly simple for us to not just observe and trace logs, but also to dive into AI-driven investigations effortlessly - turning complex Kubernetes monitoring into a smooth, intuitive experience.
Anyone running user agents on their infrastructure needs a solution like Metoro. It's just a case of when, not if a malicious agent will be running.
Support
Frequently asked
Everything about AI RCA.
What is AI root cause analysis?
AI root cause analysis uses models to automatically investigate production incidents and identify the underlying cause. Instead of manually sifting through logs, metrics, and traces, the AI correlates signals across your stack, follows dependency chains, and pinpoints the exact source of the problem - typically in minutes rather than hours.
How does Metoro's AI RCA work?
Metoro collects comprehensive telemetry using eBPF kernel hooks, capturing 100% of requests, errors, and events with no sampling. When an incident occurs, the AI correlates signals across metrics, logs, traces, and events, follows service dependencies, analyzes recent code changes, and identifies the root cause with supporting evidence.
How is this different from traditional RCA tools?
Traditional RCA tools require you to manually query logs, check dashboards, and piece together the story. Metoro's AI RCA automates this entire process - it investigates autonomously, correlates signals you might miss, and presents findings in plain English. With code repository integration, it traces issues to the exact lines of code that caused them.
Do I need to configure alerts for AI RCA to work?
No. Metoro's AI detects issues autonomously without any alert configuration. It learns normal behavior patterns and identifies anomalies automatically. You can also connect existing alerts - when they fire, the AI will investigate and provide root cause analysis. Zero configuration is required out of the box.
How long does root cause analysis take?
Metoro's AI typically completes root cause analysis in under 5 minutes, compared to an industry average of 30+ minutes for manual investigation. The AI works continuously and investigates multiple incidents in parallel, so no issue goes uninvestigated.
Can the AI fix issues automatically?
Metoro's AI generates code fixes and raises pull requests for your team to review. It operates on a permission-based model - nothing is deployed without your explicit approval. You maintain full control over what changes reach production.
What data does Metoro need for AI RCA?
Metoro generates its own telemetry using eBPF kernel hooks, so it doesn't rely on third-party observability data that may be sampled or incomplete. For code-level analysis, you can optionally connect your GitHub repositories. All data stays within your security boundary with BYOC and on-prem deployment options.
Stop losing hours to manual debugging.
Detection to remediation - automated. One-minute install. No code changes.