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AgenticOps platform

Multi-agent cloud operations, run under your team's policy

A single copilot still leaves a human running every step. CloudThinker is a multi-agent system for cloud — specialist AI agents that detect, analyze, remediate, and verify production operations together, with brokered credentials, sandboxed execution, and a tamper-evident audit on every action.

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Connect your cloud read-only. Agents start in advise mode — you promote them as they earn trust.

One AI copilot cannot run cloud operations

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. A multi-agent system solves the breadth; a governed platform solves the risk. You need both.

Too broad for one agent

Incident, cost, Kubernetes, and security are different disciplines — a monolithic assistant is shallow in all of them.

Humans still serialize

A copilot suggests; a human executes every command. MTTR stays pinned to human bandwidth.

Ungoverned action is unsafe

Standing keys, secrets in prompts, and raw data sent to third-party models are the failure modes.
The DARV loop

How the multi-agent system runs operations

Every CloudThinker agent works the same closed loop — Detect, Analyze, Remediate, Verify — so operations move end-to-end instead of stopping at an alert.

D

Detect

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.
A

Analyze

Specialist agents investigate root cause in parallel, walking the dependency graph and scoping blast radius before any change is proposed.
R

Remediate

The right agent executes the runbook inside a sandbox with scoped credentials — as a reviewable diff or an autonomous action within its granted guardrail.
V

Verify

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.

Governed by design

Why it is safe to let agents act on production

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.

Brokered credentials

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.

Sandboxed execution

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.

Deterministic tokenization

Sensitive data is tokenized deterministically before it leaves your boundary, keeping PII and secrets out of any third-party model while preserving referential consistency.

Tamper-evident audit

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.

Meet the specialist agents

Each domain gets an expert agent. They share one policy, one audit trail, and one platform — so the multi-agent system stays coordinated, not chaotic.

Incident & AI SRE

Deep Response Engine

The on-call agent team — detect, analyze, resolve, and validate production incidents, with memory that makes each one faster.

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Cloud cost

CloudKeeper + CostOps

The cost agents that find waste, right-size continuously, and cut spend to outcomes — under the same approval policy as everything else.

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Security

Oliver + Cyber

The security agents that triage findings, reason about reachability, and drive fixes — without shipping your code or secrets to a third party.

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What a multi-agent operations team delivers

Moving 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 FAQ

What is multi-agent cloud operations?

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 delivers this as an AgenticOps platform: agents run the DARV loop (Detect, Analyze, Remediate, Verify) with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit.

How is a multi-agent system for cloud different from a single AI copilot?

A single copilot answers questions and suggests commands, but a human still runs every step. A multi-agent system splits the work across domain specialists that can 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.

Is it safe to let multiple AI agents act on production infrastructure?

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–L4) per agent and per environment, so agents earn broader authority only after they demonstrate reliable, reviewed outcomes.

What is graduated autonomy for cloud agents?

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, one environment at a time. Engineers stay on the loop: they set the policy and review outcomes rather than execute every step.

How do I get started with CloudThinker multi-agent cloud operations?

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 multi-agent system run your own incident, cost, or Kubernetes scenario.

Give production a multi-agent team

Connect your cloud, watch the agents detect and analyze in read-only mode, and promote them as they earn trust. Engineers stay on the loop — the platform does the rest.

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