CloudThinker's Frontier Investigation Agents find the why behind every production issue and propose the fix. Your engineers approve. Every action is scoped, sandboxed and audited.
The problem
More telemetry, more tools, and more automation scripts haven't closed the gap. What's missing is an execution layer that can act on the signal safely.
Observability and AIOps keep getting better at surfacing signal, but the human on call is still the bottleneck. Toil rises, MTTR stalls, and the night shift never ends.
Incident, cost, Kubernetes, and security each live in a different tool. Every response is a manual relay across dashboards, runbooks, and tribal knowledge.
Handing a scripted bot production credentials, or piping sensitive telemetry to a third-party model, is exactly the failure mode the incident reports keep documenting.
How it works
Every CloudThinker agent runs the same closed loop (Detect, Analyze, Remediate, Verify) so a signal becomes a reversible, verified production change instead of another ticket.
Agents ingest your existing signal (logs, metrics, traces, alerts) and cluster the noise into a single, actionable issue.
They investigate root cause in parallel, walking the dependency graph and retrieving prior resolutions from agent memory.
They execute the matching runbook inside a sandbox with brokered, scoped credentials, under the autonomy level you set.
They confirm the fix held, capture proof, and write a tamper-evident record, then feed the outcome back into memory.
New to the model? Read what the DARV loop is, what engineers on the loop means, and how graduated autonomy works.
Why it's safe
Agents that touch production only stay safe with the right controls underneath. CloudThinker ships them as the platform default, not an afterthought.
Scoped identity is issued per task at execution time and never lives in a prompt. Nothing standing, nothing to leak.
Agents act inside an isolated environment where the credential lives in the sandbox, not the model context.
Sensitive data is tokenized deterministically at egress, so production PII never reaches a third-party model.
Every action, approval, and outcome is written to an append-only, tamper-evident log you can replay.
Promote each workflow from notify to fully autonomous within a guardrail, one at a time, as it earns trust.
Your team approves policy and guardrails and reviews outcomes, not every individual keystroke.
What changes
When agents own the loop, the human role shifts from investigating every alert to approving guardrails and reviewing verified results.
Lower MTTR
Agents investigate and remediate in parallel, so time-to-resolve drops per workflow instead of waiting on a human relay.
Less toil
Recurring, paged incidents get resolved autonomously, freeing the on-call rotation for engineering work that compounds.
Full audit trail
Every autonomous action lands in a tamper-evident record, so security and compliance reviews stop being a scramble.
Five agents, one platform
Review, Cyber, Resolve, Optimize and Customize run under the same governance. Connect your stack, encode your runbooks as skills, and promote them from notify to autonomous.
Finds the why behind production incidents, in minutes, and proposes the fix. Agent memory makes the next one faster.
Incident responsePays only for what you use. Continuous rightsizing that finds waste and proposes the change, under the same policy and audit trail.
Cloud costHacks you before hackers do. Triages findings, correlates exposure and proposes fixes, with sensitive data tokenized on the way out.
SecuritySee the full CloudThinker platform, learn what an AI SRE is, or read up on autonomous incident response.
FAQ
Agents for cloud operations are autonomous AI agents that run production cloud work end to end (detecting an issue, analyzing root cause, remediating it and verifying the fix) instead of only surfacing an alert for a human to act on. CloudThinker's Frontier Investigation Agents work this way. They find the why behind every production issue and propose the fix, and your engineers approve. They operate under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and a tamper-evident audit trail, so autonomy stays safe and reviewable.
Observability collects telemetry and AIOps compresses it into a cleaner alert, but both stop at the human handoff: an engineer still investigates and acts. Agents for cloud operations take that compressed signal as input and carry the work through to a reversible, approved production change. AIOps stops at the alert; CloudThinker agents run the DARV loop: Detect, Analyze, Remediate, Verify.
Every agent runs under graduated autonomy, from L1 (notify only) to L4 (fully autonomous within a guardrail), so you promote each workflow as it earns trust. Credentials are brokered per task rather than embedded in prompts, execution happens in an isolated sandbox, sensitive data is tokenized deterministically at egress, and every action is written to a tamper-evident audit log. Engineers stay on the loop and approve the guardrails, not every keystroke.
Common starting points are incident response and root-cause investigation across application, cloud, database and Kubernetes (Resolve), cloud cost and rightsizing (Optimize), security testing and remediation (Cyber), and pre-merge change review (Review). You connect your stack, encode your runbooks as skills, and promote them one at a time from notify to autonomous.
No. CloudThinker composes on top of the observability and alerting you already run. Your existing signal from Datadog, Prometheus, Grafana, Splunk, PagerDuty and similar tools becomes the input the agents reason over. You keep your ingest layer and add an autonomous action layer above it through Connections.
You can start free. Connect a read-only environment, watch the agents detect and analyze in notify mode, then promote the workflows you trust to remediate and verify autonomously. If you would rather see it mapped to your stack first, book a demo and we will walk through the DARV loop, graduated autonomy, and the audit trail on your environment.
Connect a read-only environment and watch the agents find the why and propose the fix. Your engineers approve, under your policy, with a full audit trail.