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AI agents that manage your infrastructure, review code, resolve incidents, and optimize costs — across multi-clouds, Kubernetes, and everything in between. Self-healing infra, autonomous.
Quickstart
Connect your cloud and run your first operation in 5 minutes
CloudThinker Language
Master the @agent and #tool syntax for effective prompting
Cloud teams juggle 8–12 separate platforms for cost, security, monitoring, and operations. When a service goes down at 3 AM, engineers spend more time correlating dashboards than fixing the problem. Cost reviews happen monthly. Security audits take weeks. And every new cloud service adds another point tool to manage.Three compounding problems:
Problem
Reality
Too many tools
Cost Explorer for spend, Security Hub for compliance, Datadog for monitoring, kubectl for containers — and none of them talk to each other
Too slow
Manual root-cause analysis takes hours; incidents stay open while engineers piece together the story across six dashboards
Too hard
Deep cloud expertise required just to ask basic questions — a permanent blocker for developers and non-specialists
Most cloud management tools are dashboards — they show you data, but you still have to interpret it, decide what to do, and manually execute changes.CloudThinker acts.
Capability
Traditional Tools
CloudThinker
Cost visibility
Show historical spend charts
Identify waste, recommend right-sizing, implement with approval
AI-powered PR reviews with security analysis — 96% accuracy
Incident
Automated detection, triage, and resolution — MTTR under 5 minutes
HelpDesk
Tiered support with intelligent escalation — 70% auto-resolved
Infra Ops
Unified cost, security, and performance — 325+ operations
CloudKeeper
24/7 autonomous monitoring and compliance
SlackOps
Conversational ops directly in Slack
Code Review
Incident
HelpDesk
Infra Ops
CloudKeeper
SlackOps
AI agents review every pull request for bugs, security vulnerabilities, and best-practice violations — then post actionable feedback directly in your PR. Security analysis runs automatically alongside code quality checks, catching issues before they reach production.
From alert to resolution in minutes. Automatically detects anomalies, triages severity, correlates related events, and executes remediation runbooks — reducing mean time to resolution (MTTR) to as low as 4m 32s.
A multi-tier support system where AI agents handle routine requests while intelligently escalating complex issues to the right specialist. 70% of tickets are auto-resolved without human intervention.
Three pillars in one unified interface: Cost optimization to eliminate waste, Security posture management to stay compliant, and Performance monitoring to keep systems healthy. Agents continuously analyze and recommend improvements across all three.
Always-on autonomous agents that continuously scan your cloud environment for drift, compliance violations, and optimization opportunities. CloudKeepers work around the clock — surfacing issues before they become incidents.
Run cloud operations directly from Slack with native conversational UI (SlackOps). Mention an agent, describe what you need, and get results — all without leaving your team’s workspace.
Every agent capability is defined as a composable Skill — a combination of SKILL.md definitions, tool bindings, prompts, guardrails, triggers, and schemas. Skills are the building blocks that make agents specialized.
Control how independently agents operate: L1 Notify (report only), L2 Suggest (recommend actions), L3 Approve (act with approval), L4 Autonomous (full self-directed execution). Each level is gated by RBAC policies.
Agents execute in ephemeral, isolated sandbox environments — microVMs with per-tenant VPC isolation that auto-destroy after use. Agent actions never touch your production infrastructure directly.
Pre-built operational procedures agents execute step-by-step. 325+ operations available, schedulable via cron, chainable for complex workflows, and customizable for your environment.
An MCP-based integrations hub connecting agents to your infrastructure. AWS, Azure, GCP, Kubernetes, Slack, GitHub, Datadog, Grafana, and more — all through a standardized protocol.
Agents build and query a vectorized knowledge base from your docs, runbooks, past incidents, and operational history. Continuous learning through RAG means increasingly accurate recommendations over time.
A visual resource graph mapping relationships across regions and providers. Topology powers Root Cause Analysis — when something breaks, agents trace the impact path instantly.
Multi-layer memory gives agents persistent context: Episodic (past interactions), Working (current task), Semantic (learned concepts), and File (document storage). Agents remember your preferences and past decisions.
Input and output safety gates protect every agent interaction — PII detection, schema validation, prompt injection defense, and content filtering. Agents always operate within defined boundaries.
Full platform monitoring and evaluation powered by OpenTelemetry. Track agent performance, trace execution, evaluate output quality with LLM-as-Judge, and monitor system health through built-in dashboards.
@alex analyze EC2 instances with <20% CPU utilization over 30 days@oliver audit security groups for public access on database ports@tony #dashboard database performance metrics for production cluster@kai optimize pod resource allocation across all namespaces@anna coordinate quarterly infrastructure review with all agents
@alex analyze spending trends over last quarter@alex #recommend reserved instance purchases for stable workloads@alex identify unattached volumes and unused elastic IPs
Typical outcome: 30–50% cost reduction with automated implementation plans
@oliver perform SOC 2 Type II compliance assessment@oliver audit IAM policies for privilege escalation risks@oliver #report security posture with remediation timeline
Typical outcome: 90%+ compliance score with complete audit trail
@tony analyze slow queries on production PostgreSQL@tony #dashboard query performance with P95 latency trends@tony recommend index optimizations for high-frequency queries
@kai analyze pod resource utilization across all clusters@kai identify nodes with <30% CPU utilization for consolidation@kai #recommend HPA policies for variable workloads
Typical outcome: 25% cost reduction with improved reliability
@anna coordinate AWS to Azure migration with all agents@anna #report quarterly infrastructure review for executive team@anna manage security remediation project across @oliver @alex @kai
Typical outcome: 75% faster project completion with unified visibility
