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AgenticOps platform

Agents for cloud operations that run production, not just watch it

CloudThinker gives your team autonomous AI agents for cloud operations — they detect issues, analyze root cause, remediate, and verify the fix, all under your policy. Brokered credentials, sandboxed execution, and a tamper-evident audit trail keep every action safe and reviewable.

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  • DARV loop
  • Graduated autonomy L1–L4
  • Engineers on the loop

The problem

Cloud operations doesn't scale by hiring faster than alerts arrive

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.

Alert volume outruns your team

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.

The work is spread across ten consoles

Incident, cost, Kubernetes, and security each live in a different tool. Every response is a manual relay across dashboards, runbooks, and tribal knowledge.

Naive automation is a liability

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

The DARV loop: agents that carry the work through to done

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.

01

Detect

Agents ingest your existing signal — logs, metrics, traces, alerts — and cluster the noise into a single, actionable issue.
02

Analyze

They investigate root cause in parallel, walking the dependency graph and retrieving prior resolutions from agent memory.
03

Remediate

They execute the matching runbook inside a sandbox with brokered, scoped credentials — under the autonomy level you set.
04

Verify

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 AgenticOps means, and how graduated autonomy works.

Why it's safe

Autonomy under team policy, with governance built in

Agents that touch production only stay safe with the right controls underneath. CloudThinker ships them as the platform default, not an afterthought.

Brokered credentials

Scoped identity is issued per task at execution time and never lives in a prompt. Nothing standing, nothing to leak.

Sandboxed execution

Agents act inside an isolated environment where the credential lives in the sandbox, not the model context.

Deterministic tokenization

Sensitive data is tokenized deterministically at egress, so production PII never reaches a third-party model.

Tamper-evident audit

Every action, approval, and outcome is written to an append-only, tamper-evident log you can replay.

Graduated autonomy L1–L4

Promote each workflow from notify to fully autonomous within a guardrail, one at a time, as it earns trust.

Engineers on the loop

Your team approves policy and guardrails and reviews outcomes — not every individual keystroke.

What changes

Review outcomes, not alerts

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.

One platform, specialist agents

Start with the workflow that hurts most

Every agent runs the DARV loop under the same governance. Connect your stack, encode your runbooks as skills, and promote them from notify to autonomous.

Deep Response Engine

The incident agent — detects, investigates, and resolves production incidents with agent memory that makes the next one faster.

Incident response

CloudKeeper + CostOps Agent

Continuous rightsizing and cloud cost agents that find and safely remediate spend, under the same policy and audit trail.

Cloud cost

Oliver + Cyber

Security agents that triage findings, correlate exposure, and remediate — with sensitive data tokenized on the way out.

Security

See the full CloudThinker platform, learn what an AI SRE is, or read up on autonomous incident response.

FAQ

Agents for cloud operations, answered

What are agents for cloud operations?

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. On an AgenticOps platform like CloudThinker 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.

How is this different from AIOps or observability tools?

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.

How do I keep autonomous agents from making unsafe changes?

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.

Which cloud operations workflows can agents handle?

Common starting points are incident response and root-cause investigation (Deep Response Engine), cloud cost and rightsizing (CloudKeeper and the CostOps Agent), Kubernetes operations (Kai), and security triage and remediation (Oliver and Cyber). You connect your stack, encode your runbooks as skills, and promote them one at a time from notify to autonomous.

Do I have to replace my existing monitoring and tooling?

No. CloudThinker composes on top of the observability and alerting you already run. Your existing signal — from Datadog, Prometheus, Grafana, Splunk, PagerDuty, and similar — becomes the input the agents reason over. You keep your ingest layer and add an autonomous action layer above it through Connections.

How do I get started with agents for cloud operations?

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.
Put agents on your cloud operations

Stop responding to alerts. Start reviewing outcomes.

Connect a read-only environment, watch the agents detect and analyze, then promote the workflows you trust to remediate and verify — under your policy, with a full audit trail.

  • Start free, read-only
  • No stack replacement
  • Tamper-evident audit
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