Testkube AI Overview
Testkube AI brings intelligent automation and assistance to your continuous testing pipelines. Whether you're debugging failed tests, automating remediation, or integrating AI assistants into your development environment, Testkube AI provides the tools you need.
Core Capabilities
Testkube AI consists of three complementary components that work together to enhance your testing workflows:
AI Agents
Automated intelligent task execution for your testing pipelines
TestkubeAI Agents are customizable AI-powered workers that can perform complex analysis and automation tasks within your Testkube environment. They leverage Large Language Models (LLMs) combined with MCP (Model Context Protocol) tools to execute multi-step workflows autonomously.
Example Use Cases:
- Automated Remediation — Analyze failed tests, identify root causes, and suggest or apply fixes automatically (see Example)
- Intelligent Analysis — Detect flaky tests, analyze execution patterns, and surface anomalies (see Example)
- Custom Workflows — Define agents tailored to your specific testing needs and integrate with external systems
AI Assistant
Interactive AI help integrated into the Testkube Dashboard
The Testkube AI Assistant is your intelligent companion within the Testkube Dashboard, providing real-time help with log analysis, configuration, navigation, and general guidance. It understands your Testkube context and can help you work more efficiently.
Example Use Cases:
- Log Analysis & Debugging — Quickly understand why tests failed with AI-powered log summarization
- Configuration Help — Get assistance writing TestWorkflow YAML with context-aware suggestions
- Smart Navigation — Find settings, workflows, and executions using natural language queries
- Testkube Guidance — Ask questions about Testkube concepts and get answers with documentation references
Access: Click the AI Assistant button in the bottom-left corner of the Testkube Dashboard.
Learn more about AI Assistant →
Testkube MCP Server
Connect AI tools in your IDE directly to Testkube
The Testkube MCP Server implements the Model Context Protocol standard, enabling AI Agents and tools like GitHub Copilot, Cursor, and Claude to interact directly with your Testkube workflows, executions, and artifacts.
Example Use Cases:
- IDE Integration — Use AI assistants in VS Code, Cursor, or other tools with full Testkube context
- Natural Language Testing — Run workflows, analyze failures, and manage tests using conversational prompts
- Agentic Workflows — Enable multi-step AI-driven debugging and test automation from your development environment
Learn more about the MCP Server →
Getting Started
For a quick start, follow these steps:
- Enable AI features for your organization - Configuration Quick Start
- Get AI help in the Dashboard - AI Assistant Overview
- Automate analysis with AI Agents - AI Agents Overview
- Connect your IDE to Testkube - MCP Server Overview
Architecture & Security
- Architecture Overview - Understand how Testkube AI components work together
- AI Assistant Security - Learn about authentication, authorization, and data privacy
- MCP Server Security - Security considerations for MCP integrations
Configuration
For detailed setup instructions including LLM configuration, authentication, and deployment options: