Frontier Investigationfor Every Alert

Every alert from every monitoring tool investigated to root cause before a human opens it.

Connect the monitoring tools you already run. Agents pick up each alert, pull the logs, metrics, traces and recent changes behind it, and hand your on-call a root cause with the evidence attached. Noise is closed with a written reason. Real issues arrive with a fix ready to approve.

CloudWatch alarmroot cause found

checkout-api p99 latency above 2s

Signals
RDS CPU at 94%, 4,210 slow queries, error rate flat
Change
Deploy v2.14.0 at 09:12 ran migration 0193
Root cause
Migration dropped the index on orders.customer_id
Evidence
6 linked queries, traces and diffs
Fix
Recreate the index online. Reversible, no rollback needed

Investigated in 94s. Waiting for on-call approval

[The work behind every alert]

Alerts fire in seconds.Root causes still take hours.

01The manual work

Investigate

An alert is only the start. On-call still opens every dashboard, pulls logs, checks the last deploy and pieces together what went wrong.

02The agent handoff

Root cause ready

Frontier agents take the alert the moment it fires, connect the evidence and prepare a root cause and a fix for your team to review.

03Your engineers’ role

Approve

Set what agents may close on their own. Review the proposed fix and decide what reaches production.

[Where CloudThinker fits]

Your stack stays.Agents work inside it.

01Signal sources

Already in your monitoring

  • AWSAmazon CloudWatchAlarms, logs and X-Ray traces
  • AzureAzure MonitorAlerts, Log Analytics, Application Insights
  • Google CloudGoogle Cloud MonitoringAlerting policies and Cloud Logging
  • DatadogDatadogMonitors, APM and logs
  • PrometheusPrometheus and GrafanaAlertmanager rules and dashboards

No change to your alert rules

02Paging and change

Your system of record

  • PagerDutyPagerDutyIncidents and escalation policy
  • OpsgenieAlert routing and schedules
  • GitHubGitHub and GitLabDeploys, diffs and pipelines
  • Argo CDArgo CDKubernetes rollouts and history

Source of truth stays put

03Investigation

CloudThinkerCloudThinker

  • CorrelateLogs, metrics and traces in one timeline
  • Trace the changeThe deploy, config or infra edit behind it
  • ProveEach finding linked to its query, trace or diff
  • ProposeA reversible fix at your approval level

Read-only by default

04Response

Fixes, not pages

  • SlackSlackRoot cause posted to the incident channel
  • Microsoft TeamsMicrosoft TeamsApprove the fix from the thread
  • JiraFollow-up ticket with the evidence
  • PagerDutyPagerDutyIncident resolved once the fix is verified

Fix runs on approval

Logos show common stacks. CloudThinker connects to each one through read-only access you approve.

[Example scenario]

Tuesday, 02:14. Checkout slows down.Your on-call is still asleep.

A 40-engineer e-commerce team on EKS and RDS, with CloudWatch, Datadog and PagerDuty. One engineer on call, a sale starting at 08:00.

  1. 02:14

    Alarm firesSignal

    CloudWatch: checkout-api p99 latency above 2s for 5 minutes. PagerDuty opens an incident.

  2. 02:14

    Agent picks it upAgent

    Groups 3 related alerts, pulls RDS metrics, slow query logs, traces and the last 24 hours of deploys.

  3. 02:15

    Root cause, with proofAgent

    Deploy v2.14.0 ran migration 0193, which dropped the index on orders.customer_id. Linked: the diff, the query plan, the trace.

  4. 02:16

    Fix proposedAgent

    Recreate the index online. Reversible, no rollback of the release needed. Posted to the incident channel and paged on-call.

  5. 02:21

    On-call approves from bedYour team

    Reads the evidence on a phone, taps Approve. The agent runs the change under the on-call’s approval.

  6. 02:29

    Verified and written upAgent

    p99 back under 300ms. Incident resolved, postmortem draft and a ticket to add a migration check left for the morning.

#inc-checkout-latency4 messages
  • PagerDuty02:14

    Triggered: checkout-api p99 latency above 2s (CloudWatch).

  • CloudThinker02:16

    Root cause: migration 0193 in deploy v2.14.0 dropped idx_orders_customer_id. 4,210 slow queries since 02:09. Proposed fix: CREATE INDEX CONCURRENTLY. Reversible. Evidence: 6 links.

  • On-call SRE02:21

    Checked the query plan. Approved.

  • CloudThinker02:29

    Index rebuilt. p99 at 280ms and steady for 5 minutes. Incident resolved. Postmortem draft and follow-up ticket CHK-1182 created.

from alarm to approved fix
7 min
dashboards opened by a person
0
from the on-call engineer
1 tap

An illustrative example. Team, systems and times are representative, not a specific customer.

[Frontier investigation agents]

Every alert gets a first look.Only the real ones reach a person.

Agents work every alert source on the same policy, so a page at 3am arrives already investigated, and a noisy rule gets flagged instead of muted.

Correlate every signal
Logs, metrics, traces and related alerts pulled into one timeline, across every tool the alert touches.
Trace it to a change
Deploys, config edits and infrastructure changes lined up against the moment things went wrong.
Close the noise
Noisy alerts closed with a written reason and the evidence behind it. Repeat offenders flagged for tuning.
Hand over a fix
Real issues come with a reversible fix that runs only at the approval level you set.

[What changes]

Same team. Same tools.Far less of the work by hand.

MomentTodayWith frontier agents
First look at an alertWhenever on-call gets to itSeconds after it fires
Gathering contextBy hand, tab by tabLogs, metrics, traces and changes pulled automatically
Noisy alertsMuted or ignoredClosed with a written, auditable reason
HandoverA long Slack threadOne root cause report with evidence links
What the team learnsStays in a few senior headsCaptured in every investigation

[Integrations]

Connects to the rest of your stack.Read-only to start.

  • Amazon CloudWatch
  • Datadog
  • Grafana
  • Prometheus
  • New Relic
  • Dynatrace
  • Splunk
  • Sentry
  • PagerDuty
  • Opsgenie
  • incident.io
  • ServiceNow
  • Slack
  • GitHub
  • GitLab

[Adoption path]

One pilot.Then company-wide.

The rollout follows the four phases of the AWS Cloud Adoption Framework, so it fits the plan your cloud team already runs.

  1. 01Envision

    Pick one noisy service

    Connect one alert source read-only and let agents investigate in shadow mode. Compare their root causes with your team’s.

  2. 02Align

    Agree the autonomy policy

    Decide which alerts agents may close alone, which fixes need approval, and who approves.

  3. 03Launch

    Roll out team by team

    Add each team’s alert sources on the same policies, report format and audit trail.

  4. 04Scale

    Make it the default

    New services launch with investigation on. Findings feed postmortems, runbooks and alert tuning.

[Customer proof]

F88.Results on the record.

A Vietnamese consumer-finance company with 800+ branches grew a small read-only pilot into a governed hybrid-cloud operating model.

Read the case study
of daily operations automated
80%
lower AWS spend
30%

[Trust and control]

Agents do the work.Your team keeps control.

You approve every change
Agents propose. Nothing touches production until someone on your team says yes, and you set that rule per system.
Every action on the record
Each step is logged, attributed and reversible, ready for your auditors.
Certified for enterprise
SOC 2 Type II and ISO 42001, with reports in our trust center.
Runs where you need it
In our cloud, through AWS Marketplace, or inside your own account.

[Questions]

What teams askbefore they start.

Does this replace our monitoring tools?
No. Agents read from the tools you already run, such as CloudWatch, Datadog, Grafana and PagerDuty. Your alert rules and dashboards stay where they are.
What access does it need?
Read-only access to the alert sources, observability data and repositories you choose. Access is scoped per source and you can revoke it at any time.
Can agents act without a person?
Only where your policy allows it. Most teams start with agents investigating and proposing, then let them close noise and run low-risk fixes once the results have earned trust.
How does it handle noisy alerts?
Each noisy alert is closed with a written reason and the evidence behind it. Repeat offenders are flagged so your team can tune or delete the rule.

Put an agent on every alert.Keep your engineers for the real ones.

Start with one noisy service, read-only. See the root causes agents find before you grant a single permission more.

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    Covered 24/7, on your approval

    Under HIPAA, GDPR and SOC 2 controls.