CloudThinker's Frontier Investigation Agents work as one team across application, cloud, security, database and Kubernetes. They find the why and propose the fix. Your engineers approve.
Connect your cloud read-only. Agents start in advise mode, and you promote them as they earn trust.
Production operations are too broad for one model and too risky for ungoverned automation. Incidents, cost, Kubernetes, and security each need a specialist, and none of them can be trusted with standing credentials, unbounded scope, or your raw data. Specialist agents solve the breadth. A governed platform solves the risk. You need both.
Incident, cost, Kubernetes, and security are different disciplines. A monolithic assistant is shallow in all of them.
A copilot suggests; a human executes every command. MTTR stays pinned to human bandwidth.
Standing keys, secrets in prompts, and raw data sent to third-party models are the failure modes.
Every CloudThinker agent works the same closed loop (Detect, Analyze, Remediate, Verify) so operations move end-to-end instead of stopping at an alert.
Agents cluster the firehose of alerts, logs, metrics, and events into real incidents and cost or security signals, so the team stops paging on noise.
Specialist agents investigate root cause in parallel, walking the dependency graph and scoping blast radius before any change is proposed.
The right agent executes the runbook inside a sandbox with scoped credentials, as a reviewable diff or an autonomous action within its granted guardrail.
Agents confirm the fix held, roll back if it did not, and write a tamper-evident record so the next incident starts smarter than the last.
New to the loop? Read what the DARV loop is and how graduated autonomy works.
Autonomy without controls is the failure mode. Every agent action passes the same four gates, and autonomy is graduated per agent and per environment, so engineers stay on the loop.
Every agent gets short-lived, per-task credentials scoped to exactly what the action needs. Never a standing key, never a secret in the prompt.
Agent actions run in an isolated environment where the credential lives in the sandbox, not the model context, so a bad instruction cannot reach production directly.
Sensitive data is tokenized deterministically before it leaves your boundary, keeping PII and secrets out of any third-party model while preserving referential consistency.
Every agent decision and action is logged to an append-only, tamper-evident record: a complete trail for review, incident forensics, and compliance.
See how CloudThinker keeps engineers on the loop across the platform.
Review, Cyber, Resolve, Optimize and Customize each own a domain. They share one policy, one audit trail and one platform, so the agents stay coordinated.
Finds the why behind production incidents, in minutes, and proposes the fix. Memory makes each one faster.
Learn moreCloud costPays only for what you use. Finds waste and right-sizes continuously, under the same approval policy as everything else.
Learn moreSecurityHacks you before hackers do. Triages findings, reasons about reachability and proposes fixes, without shipping your code or secrets to a third party.
Learn moreMoving the bottleneck off the human in the loop changes the shape of on-call, cost, and security work.
Lower MTTR
Parallel investigation and sandboxed remediation move the bottleneck off the human in the loop.
Less toil
Agents absorb the repetitive detect-and-triage work so engineers spend time on the hard, novel problems.
24/7 coverage
A multi-agent team that does not sleep. The night shift runs itself, on the loop, under policy.
Related reading: what is AI SRE and autonomous incident response.
Multi-agent cloud operations is the practice of running production cloud operations through a coordinated system of specialist AI agents rather than a single monolithic assistant. Each agent owns a domain (incidents, cost, Kubernetes, security) and they collaborate under a shared team policy, escalating to human engineers when a decision exceeds their granted autonomy. CloudThinker builds Frontier Investigation Agents for this. They find the why behind every production issue and propose the fix, your engineers approve, and they run the DARV loop (Detect, Analyze, Remediate, Verify) with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit.
A single copilot answers questions and suggests commands, but a human still runs every step. Specialist agents split the work by domain and act in parallel, under policy. One agent triages the incident while another checks blast radius and a third validates the fix. The platform coordinates them, enforces the guardrails, and records what each agent did, so you get end-to-end operations instead of a smarter autocomplete.
It is safe only when the platform enforces production-grade controls on every agent action. CloudThinker issues short-lived brokered credentials scoped to each task, runs agent actions inside sandboxed execution where the credential lives in the environment (not the prompt), tokenizes sensitive data deterministically before it leaves your boundary, and writes a tamper-evident audit record for every step. Autonomy is graduated (L1 to L4) per agent and per environment, so agents earn broader authority only after they demonstrate reliable, reviewed outcomes.
Graduated autonomy is a per-agent, per-environment authority ladder from L1 (read-only / advise) to L4 (act autonomously within a defined guardrail). New agents start at low autonomy, proposing actions a human approves. As an agent proves reliable on a class of work, the team promotes it, one skill and one environment at a time. Engineers stay on the loop: they set the policy and review outcomes rather than execute every step.
Connect your cloud and observability tools, and the platform bootstraps the relevant agents in read-only mode so you can watch them detect and analyze before they touch anything. You start free, keep every agent on a low autonomy level, and promote agents as they earn trust. Book a demo to see the agents run your own incident, cost, or Kubernetes scenario.
Connect your cloud, watch the agents investigate in read-only mode, and promote them as they earn trust. They find the why and propose the fix. Your engineers approve.