Automate Kubernetes Management with AI
Build an AI-powered Kubernetes operations assistant that monitors clusters, diagnoses issues, and executes remediation workflows automatically.
AI Agent Framework
Core AI agent that reasons about cluster state and decides on actions
Graph-based agent architecture maps naturally to K8s decision trees — diagnose, plan, execute, verify — with built-in state management for multi-step remediation workflows
Strong enterprise integration and plugin system makes it easy to wrap kubectl, Helm, and cloud provider CLIs as agent skills
Multi-agent setup allows specialized roles like cluster-monitor, incident-responder, and capacity-planner working in coordination
Tool Integration & Execution
Connect the AI agent to Kubernetes APIs, cloud CLIs, and infrastructure tools securely
Pre-built toolkits for cloud providers and infrastructure services, with managed auth — lets the agent call kubectl, AWS/GCP APIs, and monitoring endpoints without custom glue code
Sandboxed execution environment ensures that AI-generated kubectl commands and Helm operations run safely without risking accidental cluster damage
Workflow Orchestration
Orchestrate multi-step K8s operations like rolling updates, scaling, and incident response as durable workflows
Durable execution guarantees that long-running K8s operations (rolling deploys, migration drains, canary rollouts) complete reliably even if the agent process restarts
Python-native workflow orchestration with retries and scheduling — ideal for recurring cluster maintenance tasks like node rotation and certificate renewal
Visual workflow builder with Kubernetes and cloud provider nodes for teams that want low-code automation alongside AI-driven decision making
LLM Gateway & Observability
Route AI requests across providers with cost control, and observe agent decisions for audit trails
Unified LLM gateway lets you switch between models for different tasks — fast model for routine health checks, powerful model for complex root cause analysis — with spend tracking
Traces every agent decision and tool call, creating an audit log of what the AI did to your cluster and why — critical for post-incident review
Knowledge & Memory
Store runbooks, past incidents, and cluster topology so the agent learns from operational history
Persistent memory layer lets the agent remember past incidents, known failure patterns, and cluster-specific quirks — avoiding repeated misdiagnoses
RAG pipeline over internal runbooks, post-mortems, and K8s docs ensures the agent follows your team's established procedures rather than generic advice
Lightweight vector store for embedding and retrieving Kubernetes event logs, alert histories, and configuration snapshots for context-aware troubleshooting
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