Overview

AI-native incident management that investigates outages, not just alerts you

Incident response should be about solving problems, not finding them.

Your monitoring tools are excellent at detection—they catch anomalies in milliseconds and route alerts to the right engineer. But when an alert fires at 3 AM, you still need to pull up six dashboards, correlate timestamps across services, check recent deployments, and piece together the story yourself.

CloudThinker Incidents is different. When an incident occurs, an AI agent begins investigating immediately—forming hypotheses, gathering evidence, and identifying root causes the way an experienced engineer would. By the time you open your laptop, the investigation is already underway.

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How Existing Incident Tools Compare

Detection and alerting are solved. Investigation and resolution are not.

Tool	What It Does	What’s Missing
PagerDuty / Opsgenie	Routes alerts to on-call engineers	Routing only — you still investigate manually after being paged
VictorOps / Splunk On-Call	Alert routing + basic runbooks	Runbooks require manual triggering and expert interpretation
Blameless / Rootly / FireHydrant	Incident workflows and post-mortems	Process coordination, not real-time investigation
Datadog Watchdog / New Relic AIOps	Anomaly detection and alert correlation	Surfaces related alerts, but does not form hypotheses or investigate
AWS CloudWatch Alarms	Threshold-based alerting	Fires alerts, no investigation capability

The gap: every existing tool tells you that something is wrong. CloudThinker is the first to tell you why — with a structured investigation, evidence chain, and remediation plan — before you’ve opened your IDE.

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What Makes This Different

Hypothesis-driven investigation: The AI doesn’t just correlate events — it forms explicit theories (“memory leak in auth service”, “connection pool exhausted”, “recent deployment regression”) and tests each one systematically.

Transparent reasoning: Every step of the investigation is visible. You can see which hypothesis was confirmed, which was ruled out, and why. No black box.

Topology-aware blast radius: CloudThinker understands your service dependency graph. When the auth service fails, it knows payment breaks, which breaks checkout — and it maps the full impact before you ask.

MTTR under 5 minutes: By the time an on-call engineer opens their laptop, the AI has already completed the investigation, ranked the most likely root causes, and suggested remediation steps.

CloudThinker Incidents dashboard showing AI-powered root cause analysis in action

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AI That Investigates

CloudThinker Incidents is AI-native. The AI isn’t a chatbot bolted onto an existing product—it’s the foundation of how incidents are analyzed and resolved.

Hypothesis-Driven Investigation

The AI forms theories about what went wrong and systematically tests each one against your data, tracking which hypotheses are confirmed or ruled out.

Transparent Reasoning

Every step is visible in real-time. See what the AI checked, what it found, and the path it took to reach its conclusion. No black box.

Structured Evidence

Metrics with before/after comparisons, logs with timestamps, deployment changes with time-to-incident calculations—all organized into a coherent chain.

Confidence Scoring

Not every investigation reaches certainty. Confidence scores tell you whether you’re looking at a definitive answer or a hypothesis that needs verification.

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How It Works

1. Incident Created

   An incident is created manually, via API, or automatically when webhook alerts arrive from your monitoring tools.

2. Investigation Begins

   An AI agent immediately starts investigating—no waiting, no manual trigger required.

3. Hypotheses Tested

   The agent forms theories (“memory leak in auth service”, “recent deployment regression”, “exhausted connection pool”) and tests each one.

4. Evidence Gathered

   Metrics, logs, traces, configurations, and deployments are collected and organized with timeline correlation.

5. Root Cause Identified

   The AI identifies the root cause with confidence scoring and transparent reasoning you can verify.

6. Remediation Suggested

   Prioritized action steps are generated—from critical fixes to improvements—ready for your team to execute.

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Topology Awareness

Your services don’t exist in isolation. When your auth service fails, everything downstream fails too—checkout breaks, mobile apps throw errors, and support tickets spike across seemingly unrelated features.

CloudThinker understands your infrastructure topology. When an incident occurs, the AI automatically:

• Identifies affected services using your service dependency map
• Calculates blast radius showing what’s broken and what’s impacted
• Investigates with context knowing that payment depends on auth, which depends on Redis, which runs on a specific cluster

You see affected services visualized in real-time, with severity-coded nodes showing the spread of impact across your infrastructure.

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Connect Everything You Already Use

CloudThinker integrates with the monitoring tools your team already relies on. We support webhooks from 15+ platforms:

Platform	What’s Supported
PagerDuty	Native field mapping for alert details and priorities
Datadog	Metrics, alerts, and event correlation
Prometheus / Alertmanager	Kubernetes-native monitoring
AWS CloudWatch	Native support for AWS infrastructure alerts
Opsgenie	Priority and description extraction
New Relic, Grafana, Splunk, Dynatrace, Sentry	And more

Each integration includes platform-specific field mapping—incident titles, descriptions, and severity levels are extracted correctly without manual configuration.

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Continuous Learning

Every incident is an opportunity to get better. CloudThinker’s agent knowledge base system captures investigation patterns so the AI improves over time.

When the agent discovers that a particular metric query is useful for diagnosing memory issues, or that a specific log pattern indicates a connection pool problem, those techniques become part of its toolkit. Your team’s operational knowledge—the hard-won insights from years of debugging production systems—gets preserved and applied automatically.

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Next Steps

Ready to start investigating incidents? Set up incident ingestion from your monitoring tools:

Webhook Integrations

Connect your monitoring platforms to auto-create incidents. Configure field mappings for PagerDuty, Datadog, Prometheus, CloudWatch, and 10+ more platforms.

Once incidents are flowing in, explore the Root Cause Analysis workflow to understand how AI agents investigate and prioritize remediation steps. You can also connect your team’s operational procedures via Runbooks so agents can find and execute remediation steps automatically.

Root Cause Analysis

Understand hypothesis-driven investigation and confidence scoring

Webhook Integrations

Connect PagerDuty, Datadog, Prometheus, CloudWatch, and 11 more platforms

Topology

Map service dependencies to enable blast radius analysis during incidents

Slack Integration

Run incident investigation commands directly from Slack