CloudThinker's Frontier Investigation Agents investigate every production incident across application, cloud, security, database and Kubernetes. They find the why in minutes and propose the fix. Your engineers approve.
Modern monitoring is excellent at telling you something broke. It is useless at fixing it. The page still lands on a tired engineer who has to context-switch, open five dashboards, walk the dependency graph, remember the runbook, and execute under pressure. Detection is solved. The bottleneck is the human in the middle who has to investigate, decide, and act, every single time, on every single incident.
AIOps compresses the noise, but the alert still just wakes a human. MTTR stays pinned to how fast one person can log in and reason under pressure.
The same incident gets re-triaged every rotation because the fix lives in someone’s head. Repeat pages burn out the on-call and defer the permanent fix.
Raw scripts and prompt-in-the-loop bots holding standing credentials are an outage and breach waiting to happen. So teams turn them off and do it by hand.
The Resolve agent doesn't stop at the alert. It runs the full DARV loop, then hands you a reviewed outcome instead of another dashboard. Every step is scoped, sandboxed and audited, and every incident makes the next one faster.
Cluster and correlate signal from your existing observability, alerting, and paging tools into a single actionable incident. No more paging on noise.
Investigate root cause in parallel: walk the dependency graph, pull prior incidents from memory, and rank the likely fix before a human even logs in.
Execute the matching runbook inside a sandbox with scoped credentials, at the autonomy level your policy allows for that incident type.
Confirm the fix held, roll back automatically if it didn’t, and write a tamper-evident receipt so the next incident starts smarter.
Go deeper: see the Resolve agent, or read what autonomous incident response and an AI SRE actually mean.
An agent acting in production during an incident only stays safe when authority is bounded, credentials are brokered, and every action leaves a receipt. CloudThinker builds that in, so you can expand autonomy without expanding blast radius.
Every runbook starts at L1: propose-and-approve. Promote it toward act-with-approval and bounded autonomy as it proves out on real incidents, per environment. Engineers stay on the loop, never off it.
No standing secrets, no keys in the prompt. Each incident task gets a time-bound, least-privilege credential that lives in the sandbox and expires when remediation is done.
Agents act inside an isolated environment. PII in logs and traces is deterministically tokenized at egress before anything leaves your boundary, protecting data across GDPR, HIPAA, and local regimes.
Every detection, decision, and change is written to an immutable log you can replay for post-mortems, reviews, and compliance. SOC 2 aligned across the platform.
Lower MTTR
Investigation and remediation run in parallel and start from prior-incident memory, so resolution time drops per runbook, not just per dashboard.
Quieter on-call
Recurring incidents get resolved autonomously inside guardrails, and repeat patterns get surfaced for a permanent fix, so the pager stops firing for the same thing.
Zero blind trust
Every action during an incident is scoped, sandboxed, and audited, so leadership can approve more autonomy with evidence, not faith.
Outcome figures depend on your environment and runbook coverage.
Finds the why behind production incidents, in minutes, with memory that compounds across incidents.
Pays only for what you use. Finds idle spend, right-sizes resources and fixes cost drift, under approval.
Hacks you before hackers do. Proves exposure, triages findings and drives the fix across cloud and app.
Wire CloudThinker into the observability, paging, and ticketing tools you already run. No rip-and-replace.
New to the category? Read what an AI SRE is or explore the full platform.
AI incident response is the practice of resolving production incidents with AI agents instead of a human working every step by hand. CloudThinker’s Frontier Investigation Agents run the full DARV loop (Detect, Analyze, Remediate, Verify), so an agent correlates the signal, investigates root cause, executes the runbook under policy, and confirms the fix held. Unlike a read-only assistant or an alert-summarizer, it takes real, reversible action inside your environment with brokered credentials, sandboxed execution, and a tamper-evident audit trail.
AIOps and observability compress the firehose into correlated alerts, and on-call tools page a human to act on them. Both stop at surfacing a problem. CloudThinker takes that signal as input and carries the response through: it investigates in parallel, picks the matching runbook, executes inside a sandbox with scoped credentials, and writes an audited receipt. AIOps ends at the alert; the Resolve agent ends at a verified, reversible production change.
Only if you allow it. Autonomy is graduated across four levels (L1–L4). Every runbook starts at L1: the agent proposes, a human approves each step. As a runbook earns trust on real incidents, you promote it toward act-with-approval and then bounded autonomy inside guardrails you define per environment. Engineers stay on the loop; the agent never holds more authority than your policy grants.
CloudThinker never puts long-lived secrets in a prompt. Each incident task gets a brokered, scoped, time-bound credential that lives inside a sandboxed execution environment, not in the model context, and expires when the work is done. Sensitive data such as PII in logs is deterministically tokenized at egress before anything leaves your boundary, and every detection, decision, and change is written to a tamper-evident audit log for post-mortems and compliance.
Yes, because the bottleneck moves. Detection and investigation run in parallel and start from prior-incident memory, so the agent is not relearning last month’s outage every rotation. Remediation runs the moment the fix is approved rather than waiting on a human to page in, context-switch, and execute. MTTR drops per runbook as each one graduates to higher autonomy, and recurring incidents get surfaced for a permanent engineering fix instead of being re-triaged forever.
No. CloudThinker composes on top of the tools you already run: Datadog, Prometheus, Grafana, Splunk, PagerDuty, Opsgenie, and your ticketing system. Their signal becomes the input the Resolve agent reasons over. You keep your alerting, start with one or two runbooks, keep engineers on the loop, and expand autonomy incident type by incident type as trust builds.
Connect your cloud. Frontier Investigation Agents find the why behind every incident and propose the fix. Your engineers approve, under your policy, with a full audit trail.