Kubiya

Kubiya AI is an agentic engineering platform that uses AI to automate and orchestrate complex engineering workflows. It acts as an on-demand, policy-driven system that turns business goals (KPIs) into executable engineering outcomes, integrating with existing DevOps, cloud, and infrastructure tools.

Kubiya AI combines natural language understanding with deterministic, containerized execution. Users can request actions in plain language (e.g., “Create a dev environment for Project X”), and Kubiya orchestrates the required steps across your infrastructure, tools, and cloud providers, ensuring secure, auditable, and reproducible execution.

The main features of Kubiya AI include Agentic Engineering, which provides autonomous, specialized agents for platform, security, finance, and SRE tasks; Deterministic Workflows, ensuring structured, repeatable execution for reliability; Context-Aware Automation, which is grounded in your infrastructure, code, and data; Secure by Design, incorporating zero-trust enforcement, isolated sandboxing, and full auditability; Rich Integrations, with over 100 connectors for tools like Kubernetes, AWS, GCP, GitHub, Jenkins, PagerDuty, and Slack; Policy-Driven Execution, where every action is governed by policies such as OPA before execution; and Lifecycle Tracking, offering metrics, reporting, and ROI measurement for all automated initiatives.

Kubiya AI is intended for Platform Engineers who need reliable, secure automation and reduced maintenance; DevOps & SRE Teams seeking faster incident response, reduced toil, and cross-environment orchestration; and Business Leaders looking to translate business KPIs into engineering outcomes and measure ROI.

The benefits of using Kubiya AI include Enhanced Productivity, achieved by automating repetitive tasks and freeing teams for strategic work; Error Reduction, which minimizes human errors in routine operations; Scalability, allowing it to scale across teams and projects without added complexity; User Empowerment, enabling developers to perform complex operations independently; and Governance & Compliance, through full audit trails, policy enforcement, and secure execution.

Kubiya AI ensures security and compliance through Zero-Trust Enforcement, where every action is checked against policies; Isolated Execution, with tasks running in secure, sandboxed environments such as microVMs or containers; Auditability, as all actions are logged and traceable; Enterprise-Grade Authentication, supporting AWS IAM, OAuth2, SAML, OIDC, and JWT; and SOC2 Compliance, which includes encryption at rest and in transit.

Yes. Kubiya AI supports both cloud and on-premises deployments, including hybrid environments. It can be hosted on your infrastructure for full data control and compliance.

Kubiya AI connects natively to a wide range of DevOps, cloud, and monitoring tools, including Kubernetes, AWS, GCP, GitHub, Jenkins, PagerDuty, and Slack. It uses modern protocols like MCP and A2A, and supports any tool with an API or Docker image.

Yes. Kubiya AI supports private deployments and self-hosted LLMs, allowing full data control and compliance within your VPC.

Kubiya AI agents can autonomously respond to alerts, execute remediation workflows, and notify teams. Actions are logged, policy-scoped, and traceable, enabling trust, auditing, and debugging.

Kubiya AI agents are not just chatbots; they are task-based agents capable of executing scoped actions, such as restarting services, rolling back deployments, or checking incident status, and integrating deeply with your infrastructure.

To get started with Kubiya AI, first log in to the Kubiya platform, then connect your tools and infrastructure. Next, use natural language to request tasks, for example by saying “Show me Kubernetes clusters I have access to,” and Kubiya will execute the task safely and return results. Finally, you can customize agents and workflows as needed.