Introduction to ContextQA
An overview of ContextQA, an AI-powered test automation platform that lets teams create, run, and maintain web, mobile, and API tests using natural language.
Who is this for? Engineers, QA managers, and non-technical stakeholders who want to understand what ContextQA is, what problems it solves, and how it fits into a modern engineering team.
ContextQA is an AI-powered test automation platform designed to make software testing faster, more reliable, and accessible to every member of an engineering team. Instead of writing brittle Selenium scripts or complex Playwright code, you describe what to test in plain English and ContextQA's AI engine handles the rest — generating steps, executing them against real browsers, capturing evidence, and self-healing tests when the UI changes.
The platform is built around a multi-stage AI pipeline. Each agent handles a distinct concern: navigating the application, discovering interactive elements, executing steps, capturing screenshots, monitoring network calls, verifying assertions, and repairing broken locators. Together they produce a fully autonomous testing loop that requires minimal maintenance even as your application evolves.
Why ContextQA
Modern software teams face two compounding pressures: applications change faster than tests can be updated, and test suites grow complex enough to require dedicated automation engineers. ContextQA addresses both:
Natural language test creation — Write test instructions in plain English ("Login as admin, navigate to the reports page, verify a chart is visible"). No XPath, no CSS selectors, no code required.
Self-healing AI — When a button is renamed or a class changes, the AI automatically locates the correct element and repairs the step instead of throwing a failure.
End-to-end evidence — Every step captures a screenshot, the network HAR log, and browser console entries. A full video recording and Playwright trace are produced per execution.
Universal test targets — Web, mobile (iOS and Android), REST APIs, Salesforce, and SAP are all supported from a single workspace.
AI agent integration via MCP — ContextQA exposes 67 tools through a Model Context Protocol (MCP) server, letting AI coding assistants such as Claude, Cursor, and GPT create and run tests on your behalf.
Broad test generation sources — Generate test cases from Jira tickets, Figma designs, Excel spreadsheets, Swagger/OpenAPI specs, screen recordings, and n8n workflow definitions.
Deep integrations — Connect to Jira for defect tracking, Slack for notifications, and GitHub Actions, Jenkins, or Azure DevOps for CI/CD pipeline execution.
Platform Highlights
AI execution pipeline
Fully automated, end-to-end
Specialized AI agents
13+ agents
Supported test targets
Web, Mobile (iOS/Android), REST API, Salesforce, SAP
MCP tools exposed
67 tools
Evidence per step
Screenshot, network log, console log
Evidence per execution
Video recording, Playwright trace, root cause analysis
Self-healing
Automatic repair with high confidence
Test generation sources
Jira, Figma, Excel, Swagger, video, n8n, requirements text
CI/CD integrations
GitHub Actions, Jenkins, Azure DevOps, CircleCI
Notification integrations
Slack, email
Defect tracking integrations
Jira
Key Concepts Glossary
Understanding these terms will help you navigate the platform efficiently.
Workspace
An isolated project environment corresponding to one application under test. A workspace contains all test cases, suites, plans, environments, and settings for that application. You might have separate workspaces for your web app, your mobile app, and your internal API.
Test Case
The atomic unit of testing. A test case specifies a starting URL, a sequence of steps (authored in natural language or recorded), optional tags, and optional prerequisite test cases.
Step
A single instruction within a test case: "Click the Submit button", "Verify the confirmation email was displayed", "Call POST /api/login". Steps can be AI Agent steps, navigation steps, REST API calls, conditionals, loops, or verifications.
Step Group
A reusable collection of steps saved as a named library — similar to a function or subroutine. For example, SG_Login could contain the three steps needed to authenticate, and be inserted into dozens of test cases without duplication.
Test Suite
A logical grouping of test cases. Suites can be nested like folders. Typical examples: "Smoke Tests", "Regression Pack", "Checkout Flow".
Test Plan
An execution configuration that specifies which suites to run, which browsers or devices to target, which environment to use, whether to run in parallel or sequentially, and any recurring schedule.
Environment
A named configuration that stores a base URL and a set of key-value parameters (e.g., API keys, database hostnames). Switching environments lets the same test suite run against staging, QA, and production without modification.
Test Data Profile
A parameterized data set attached to a test case. Each row in the profile represents one complete test run with its own input values, enabling data-driven testing without duplicating test cases.
Execution
One run of a test case, suite, or plan. An execution produces a unique result_id and generates video, screenshots, logs, and a root cause analysis report.
Self-Healing
The AI capability that automatically detects when a UI element has changed and repairs the test step to reference the correct new element, provided the AI confidence score exceeds 90%.
Knowledge Base
A set of AI-readable instructions stored in the workspace that tell the test agent how to handle specific situations — closing cookie consent banners, bypassing CAPTCHA prompts in testing environments, handling multi-factor authentication flows.
Custom Agent
An AI persona with a custom system prompt. Custom agents allow domain-specific testing behavior, for example an agent calibrated for Salesforce object navigation or an agent that follows your company's specific accessibility standards.
MCP Server
The Model Context Protocol server that ContextQA runs, exposing platform capabilities as callable tools to external AI assistants. This allows Claude, Cursor, GPT, or any MCP-compatible client to create tests, run them, and retrieve results programmatically.
How the Platform Fits Together
A typical workflow on ContextQA follows this path:
Create a Workspace for your application.
Configure an Environment with the base URL and any credentials or API keys your tests need.
Create Test Cases — using AI assistance, recording, or importing from external sources.
Organize into Test Suites — group related tests so they can be run together.
Build a Test Plan — configure which suites, which browsers, and which environment.
Execute — manually for immediate feedback, or on a schedule for continuous monitoring.
Review Results — inspect screenshots, video, network logs, and AI-generated root cause analysis for any failure.
Iterate — the self-healing AI handles most minor UI changes automatically; you address genuine functional failures.
Supported Platforms
Web Browsers: Chrome, Firefox, Safari, Microsoft Edge
Mobile: iOS (physical devices and simulators), Android (physical devices and emulators)
API: REST (GET, POST, PUT, PATCH, DELETE) with variable chaining between steps
Enterprise Applications: Salesforce (Lightning and Classic), SAP
Next Steps
Follow the Quick Start guide to run your first test in 5 minutes.
Read Core Concepts for a deeper explanation of each platform building block.
Explore Platform Architecture to understand the AI pipeline in detail.
Create your first test in 5 minutes — no code required. Start Free Trial → — Or Book a Demo → to see ContextQA with your application.
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