Keep your paging where it is. CloudThinker's Frontier Investigation Agents find the why behind every incident and propose the fix. Your engineers approve, with a tamper-evident audit trail on every action.
You don't want smarter alerts. You want an agent that closes the incident.
Teams comparing AI incident-response options tend to want the same short list: an agent that resolves incidents end-to-end rather than only alerting and escalating, autonomy they can turn up gradually and trust, and an audit trail their security and compliance teams will sign off on. When AI stops at better notifications, the search for an alternative begins.
We describe CloudThinker honestly and don't publish unverified competitor claims. Here is how CloudThinker answers the three requirements that most often send teams looking past alert-and-page tooling.
Alerting, escalation, and AI summaries still leave a human to run the fix. The Resolve agent carries the incident through to a verified, reversible production change under your approval gate.
Graduated autonomy (L1–L4) lets each runbook start read-only and earn scope as it earns trust, instead of an all-or-nothing switch you can never fully turn on.
Brokered credentials, sandboxed execution, deterministic tokenization at egress, and a tamper-evident log on every action. These are the controls that get an AI-on-production rollout approved.
Want a side-by-side? See the CloudThinker vs PagerDuty comparison.
The Resolve agent runs a closed loop on every incident. Each pass is recorded, so the next incident of the same shape starts smarter than the last.
Clusters raw alerts from your observability and paging stack into a single real incident. No more responding to noise.
Runs parallel root-cause investigation across logs, metrics, traces, and the dependency graph.
Executes the matching runbook inside a sandbox with brokered credentials, under your approval gate.
Confirms the fix held, closes the incident, and writes a tamper-evident receipt of everything it did.
The agent starts read-only and earns scope per runbook, from L1 (observe and propose) to L4 (act autonomously within a guardrail). Engineers set the gate; the platform enforces it on every task.
Promote each runbook from notify, to act-with-approval, to autonomous, one at a time, as it earns trust.
Scoped credentials are issued per task and live in the sandbox. Never in the prompt, never in the model.
Every action runs in an isolated environment, so a bad step can be contained and rolled back.
Sensitive data is tokenized deterministically at egress. Production PII never leaves in the clear.
Every detection, decision, and action is recorded in an append-only, tamper-evident log.
Humans review outcomes and tune guardrails instead of driving every keystroke of the response.
Because every resolved incident lands in agent memory, recurring incidents resolve faster each time. The loop learns.
De-duplication and correlation mean the agent absorbs the alert storm so your team only sees real incidents.
Detect-analyze-remediate-verify runs around the clock, so the 2am page becomes a morning summary to review.
Want the full mechanics? Read about the Resolve agent and autonomous incident response.
CloudThinker is an alternative for teams that want AI to do more than route and page. Its Frontier Investigation Agents run the DARV loop (Detect, Analyze, Remediate, Verify) on production incidents: they find the why, propose the fix, and your engineers approve under your team's policy. The difference buyers tend to care about is governance: credentials are brokered per task, execution is sandboxed, sensitive data is tokenized deterministically at egress, and every action lands in a tamper-evident audit log. Engineers stay on the loop rather than in the middle of every alert.
Teams evaluating AI for incident response usually want the same short list: an agent that actually resolves incidents rather than only alerting, escalating, and summarizing them; safe autonomy they can turn up gradually; and an audit trail their security and compliance teams will sign off on. When AI features stop at smarter notifications and human-run runbooks, teams start comparing options for a system that closes the incident. CloudThinker is built around those three requirements: end-to-end DARV resolution, graduated autonomy (L1–L4), and tamper-evident audit by default.
CloudThinker's Frontier Investigation Agents carry an incident through to a verified, reversible production change. They detect, investigate root cause in parallel, execute the matching runbook inside a sandbox, and verify the fix held. Every task runs under graduated autonomy (L1–L4), with brokered credentials that never touch the model, deterministic data tokenization at egress, and a tamper-evident audit record. Rather than replacing alerting and on-call, CloudThinker composes on top of it. We describe CloudThinker honestly here and don't publish unverified competitor claims.
Yes. CloudThinker composes on top of your existing alerting and on-call tooling rather than ripping it out. It ingests signal from common observability and alerting tools (Datadog, Prometheus, Grafana, Splunk, ELK, PagerDuty, Opsgenie) and connects to your cloud and Kubernetes environments through brokered, scoped connections. Your paging and escalation flow can stay in place while the Resolve agent takes over the detect-analyze-remediate-verify work behind it.
Yes. Safety is the design point. Every task runs under graduated autonomy (L1–L4): the agent starts read-only and earns broader scope per runbook as it earns trust. Credentials are brokered per task and live in the sandbox, never in the prompt or the model. Execution is isolated so a bad step can be contained and rolled back. Sensitive data is tokenized deterministically at egress, and every detection, decision, and action is written to a tamper-evident audit log. Engineers set the approval gate for each environment and stay on the loop.
DARV is the four-stage loop the Resolve agent runs on every incident: Detect (cluster raw signal into a single real incident), Analyze (parallel root-cause investigation across logs, metrics, traces, and the dependency graph), Remediate (execute the matching runbook inside a sandbox under your policy), and Verify (confirm the fix held and close the incident). Because every loop is recorded, the next incident of the same shape starts smarter than the last.
Agents investigate every issue across application, cloud, security, database and Kubernetes, find the why and propose the fix. Your engineers approve, under your policies and audit trail.