If you are evaluating Resolve AI, you want autonomous incident response. CloudThinker's Frontier Investigation Agents find the why and propose the fix. Your engineers approve, and the agents verify it held, with a tamper-evident audit trail.
Teams rarely look for a Resolve AI alternative because of investigation quality. They look because of what happens after the root cause is found, and who has to be awake for it.
A great root-cause write-up still leaves the fix to a human at 3am. Buyers want an agent that carries the incident through to a reversible remediation and confirms it worked.
Autonomy in production is only acceptable with brokered credentials, sandboxed execution, data tokenization at egress, and a tamper-evident audit trail. Security and platform teams need to sign off.
The same operational surface that pages you at night also bleeds cloud spend and ships risky code. Buyers want one governed agent layer across incidents, cost and security, not a point tool per problem.
Nobody flips autonomous remediation on across the whole estate on day one. Buyers want to promote runbooks from notify to approve to autonomous, one at a time, and keep engineers on the loop.
Autonomous investigation is useful. It collapses time-to-understand and cuts escalations. But an incident-focused AI SRE stops at the Analyze phase. The engineer still has to design the fix, gather the right credentials, run it safely, and confirm nothing else broke. That is where MTTR actually lives, and it is still on the human.
The second gap is scope. The same platform that pages you also overspends and ships risk. A single governed agent layer that spans incidents, cost and security is a different proposition than a per-problem point tool, and it is why buyers keep widening the search.
Frontier Investigation Agents run a closed loop (Detect, Analyze, Remediate, Verify) with the production-side controls that make autonomous action defensible.
The agents cluster raw signal from your existing observability and alerting tools into a single incident, removing duplicate noise instead of paging on every alert.
They investigate root cause in parallel across cloud, Kubernetes, code and incident context, and find the why in minutes.
They propose the matching runbook and run the reversible fix inside a sandbox with scoped, task-time credentials, once your engineers approve.
They confirm the incident is resolved before closing, then write a tamper-evident audit record. The next similar incident starts smarter.
Every phase runs under team policy with brokered, task-time credentials, sandboxed execution, deterministic data tokenization at egress, and a tamper-evident audit trail. Graduated autonomy (L1 to L4) keeps engineers on the loop, not in the critical path of every alert.
| Dimension | Incident-only AI SRE | CloudThinker |
|---|---|---|
| Core job | Incident investigation and RCA | The full DARV loop: Detect, Analyze, Remediate and Verify |
| Ends at | A root-cause hypothesis for a human to act on | A reversible, verified, audited production change |
| Autonomy model | Investigation-focused autonomy | Graduated autonomy L1 to L4, promoted per runbook under policy |
| Production controls | Varies by vendor | Brokered credentials, sandboxed execution, tokenization at egress, tamper-evident audit |
| Scope | Incidents | Incidents (Resolve), cloud cost (Optimize), security (Cyber), code (Review) |
CloudThinker's Frontier Investigation Agents are a strong alternative for teams that want more than alert investigation. They run the full DARV loop (Detect, Analyze, Remediate, Verify): the agents find the root cause, propose a reversible fix, run it under team policy once an engineer approves and prove it held. CloudThinker also covers cloud cost, security and code review, so it is not an incident-only tool.
CloudThinker builds Frontier Investigation Agents. They find the why behind every production issue and propose the fix, and your engineers approve. They work on production cloud under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization and tamper-evident audit. The agents handle the incident investigation you would expect, then go further: they propose and run remediation runbooks with graduated autonomy (L1 to L4) and verify the outcome. The platform also extends beyond incidents into cloud cost (Optimize), security (Cyber) and code review (Review).
They remediate. Investigation and root-cause analysis are the Analyze phase of the DARV loop. The agents continue into Remediate, running the approved runbook inside a sandbox with scoped, task-time credentials, and Verify, confirming the incident is resolved before closing. Every action is logged to a tamper-evident audit trail.
Autonomous action stays safe when the platform enforces the production-side controls: per-task brokered identity, credentials issued at task time and scoped to the job, sandboxed execution where the credential lives in the environment (not the prompt), deterministic data tokenization at egress, and per-environment approval gates. CloudThinker uses graduated autonomy (L1 to L4), so each runbook earns trust before it acts without a human in the loop. Engineers stay on the loop, not in the critical path of every alert.
No. CloudThinker composes on top of your current signal layer. Alerts from tools like Datadog, Prometheus, Grafana, Splunk, PagerDuty, and Opsgenie become the input the Frontier Investigation Agents reason and act on. You keep your ingest and correlation stack and add the investigation and action layer.
Connect CloudThinker to your observability and cloud accounts. Frontier Investigation Agents find the why and propose the fix. Your engineers approve, under your policies, with a full audit trail.