CloudThinker is the AgenticOps platform for AI agents in cloud operations — autonomous agents that detect, analyze, remediate, and verify, under your team policy with brokered credentials, sandboxed execution, and a tamper-evident audit trail. Engineers on the loop, not buried in the pager.
More services, more alerts, more spend, more attack surface — and the same on-call engineers absorbing all of it. Dashboards surface problems but never act. Scripts automate the happy path and break on the exception. The bottleneck is no longer detection; it is the human in the middle who still has to investigate, decide, and execute every single time.
AIOps compresses the firehose, but a human still has to open every incident, walk the dependency graph, and act. MTTR stays pinned to human bandwidth.
Idle resources, unowned cost, and repetitive runbooks pile up faster than any rotation can clear. The work that gets deferred is the work that compounds.
Raw scripts and prompt-in-the-loop bots that hold standing credentials are a breach and outage waiting to happen. Teams turn them off — and go back to doing it by hand.
CloudThinker agents don't stop at the alert. They run the full DARV loop end-to-end, then hand you a reviewed outcome instead of another dashboard. Every step is scoped, sandboxed, and audited.
Ingest and correlate signal from your existing observability, cloud, and cost tools into a single actionable event.
Investigate root cause in parallel — walk the dependency graph, pull prior incidents from memory, and rank the likely fix.
Execute the matching runbook inside a sandbox with scoped credentials — under the autonomy level your policy allows.
Confirm the fix held, roll back if it did not, and write a tamper-evident receipt so the next incident starts smarter.
Want the full picture? See what AgenticOps is and what an AI SRE does.
Agents in production only stay 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 risk.
Every skill starts at L1 — propose-and-approve. Promote it toward act-with-approval and bounded autonomy as it earns trust, per environment. Engineers stay on the loop, never off it.
No standing secrets, no keys in the prompt. Each task gets a time-bound, least-privilege credential that lives in the sandbox and expires when the work is done.
Agents act inside an isolated environment. Sensitive data is deterministically tokenized at egress before anything leaves your boundary — protecting PII across GDPR, HIPAA, and local regimes.
Every detection, decision, and change is written to an immutable log you can replay for reviews, post-mortems, 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 skill, not just per dashboard.
Less toil
Recurring runbooks run autonomously inside guardrails, freeing the rotation for the work only humans should do.
Zero blind trust
Every action is scoped, sandboxed, and audited — so leadership can approve more autonomy with evidence, not faith.
The incident agent and AI SRE — detects, investigates, and resolves production incidents with agent memory.
Finds idle spend, right-sizes resources, and remediates cost drift — under approval, with a full audit trail.
The security agent — surfaces exposure, triages findings, and drives fixes across your cloud and app surface.
Wire CloudThinker into the observability, cloud, and ticketing tools you already run — no rip-and-replace.
New to the category? Read what autonomous incident response is or explore the full platform.
AI agents for cloud operations are autonomous software agents that run production cloud work end-to-end — detecting issues, analyzing root cause, remediating under policy, and verifying the fix. Unlike a chatbot or a read-only assistant, CloudThinker agents take real action inside your environment through the DARV loop (Detect, Analyze, Remediate, Verify), under team policy, with brokered credentials, sandboxed execution, and a tamper-evident audit trail.
Observability collects telemetry and AIOps compresses it into correlated alerts — both stop at surfacing a problem for a human to act on. CloudThinker takes that signal as input and acts: it investigates, executes the runbook inside a sandbox with scoped credentials, and writes an audited receipt. AIOps ends at the alert; CloudThinker carries the action through to a reversible, approved production change.
Yes. Autonomy is graduated across four levels (L1–L4). New skills start at L1 — the agent proposes, a human approves every step. As a skill earns trust, 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 gets more authority than your policy grants.
CloudThinker never puts long-lived secrets in a prompt. Each task gets a brokered, scoped, time-bound credential that lives in a sandboxed execution environment — not in the model context. Sensitive data is deterministically tokenized at egress before anything leaves your boundary, and every action is written to a tamper-evident audit log for review and compliance.
CloudThinker ships specialist agents for the highest-toil surfaces: incident response and AI SRE (Deep Response Engine), cloud cost (CloudKeeper + CostOps Agent), Kubernetes operations (Kai), and security (Oliver + Cyber). Connections wire the platform into your existing observability, cloud, and ticketing stack, so agents act on the tools you already run.
No. CloudThinker composes on top of the stack you already have. Your observability and alert-correlation layer stays; its signal becomes the input the agents reason over. You start with one or two runbooks, keep engineers on the loop, and expand autonomy skill by skill as trust builds.
Connect your cloud and let CloudThinker's agents detect, analyze, remediate, and verify — under your policy, with a full audit trail. Start free or book a demo.