RubixKube delivers Site Reliability Intelligence (SRI) through an autonomous platform that observes, diagnoses, and resolves infrastructure failures without human intervention. The system combines a conversational interface, a unified observability graph, and a persistent memory engine to reduce alert noise and cut mean time to understanding by 21×. It integrates natively with existing tools like Jira, Slack, and PagerDuty, enabling teams to automate incident response while maintaining human oversight.
Funding
Funding not disclosed
Founders
Product
Problem
Modern infrastructure teams face an explosion of alerts, fragmented observability data, and static runbooks that fail to keep pace with distributed, multi-cloud systems. Human attention cannot scale with the speed of releases, and existing tools show what happened but not why, leaving root cause analysis slow and error-prone. This complexity erodes reliability, wastes engineering hours, and creates existential risk for businesses that depend on uptime.
Solution
RubixKube provides a Site Reliability Intelligence (SRI) platform that autonomously detects anomalies, diagnoses root causes, and resolves failures in real time. The system uses a self-orchestrating mesh of specialized AI agents—Observer, Planner, Executor, Historian, and Collaborator—that work together to reason through multi-step infrastructure operations. A persistent Memory Engine stores every signal, session, and resolution, building a compounding model of the system that improves with each incident. The platform includes a conversational interface for natural language queries, a unified observability graph that connects metrics, logs, and events, and a desktop IDE (Kepler) that runs locally with user credentials. RubixKube enforces safety through human-in-the-loop controls, explainable reasoning, and policy-driven guardrails, ensuring autonomous actions remain auditable and reversible.
Target Audience
Primary users are SREs, DevOps engineers, platform engineers, staff engineers, tech leads, CTOs, and heads of engineering who need explainable, safe, and scalable reliability automation. The platform also serves founders, risk and compliance teams, and organizations managing multi-cloud or microservices architectures.
Features
- Agentic Mesh Architecture: A self-orchestrating network of specialized AI agents (Observer, Planner, Executor, Historian, Collaborator) that collaborate as a reasoning engine for multi-step infrastructure operations
- Memory Engine: Persistent, tenant-private storage of incident history, dependency maps, and human corrections that compounds over time to improve future recommendations
- Conversational Interface: Context-aware natural language UI that lets engineers ask questions, run commands, and review incident timelines without CLI gymnastics
- Unified Observability Graph: Live graph of services, metrics, logs, dependencies, ownership, and incident history, enriched with CI/CD context and runbooks
- Automated Remediation: Agents that diagnose, plan, and act with human-in-the-loop controls, supporting Observe, Assist, or YOLO autonomy levels
- Native Integrations: Day-one connectors for Jira, GitHub, PagerDuty, Slack, Confluence, and more, with no new agents to deploy
- Rubix CLI: Terminal-based intelligence for investigating incidents, understanding blast radius, and getting RCAs without leaving the command line
- Kepler Desktop IDE: Local AI agent that learns systems, remembers incidents, and works clusters with user credentials, ensuring nothing leaves the machine