CloudThinker's Frontier Investigation Agents find the why behind every production incident and propose the fix. Your engineers approve, under your policy, with a tamper-evident audit trail on every action.
You don't want another tool that pages you. You want one that closes the incident.
Teams comparing AI incident-response tools tend to want the same short list: an agent that resolves incidents end-to-end rather than only investigating them, autonomy they can turn up gradually and trust, and an audit trail their security and compliance teams will sign off on. When any one of those is missing, 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 for an alternative.
An agent that investigates but stops short of acting leaves MTTR bottlenecked on a human. 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 Cleric 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 stack into a single real incident. No more paging on 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 evaluating an AI SRE for incident response. 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 action.
Teams evaluating AI incident-response tools usually want the same short list: an agent that actually resolves incidents rather than only investigating them; safe autonomy they can turn up gradually; and an audit trail their security and compliance teams will sign off on. When one of those is missing (for example, an agent that can read your systems but not act on them under a clear approval gate), teams start comparing options. 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. We describe CloudThinker honestly here and don't publish unverified competitor claims.
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.
No. CloudThinker composes on top of your existing stack rather than replacing it. 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. The AIOps signal layer you already run becomes the input the Frontier Investigation Agents reason over.
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.