# AI Features Overview

{% hint style="info" %}
**Who is this for?** Testers, SDETs, and QA managers who want to leverage AI to generate tests faster, execute them autonomously, and reduce maintenance through self-healing and intelligent context awareness.
{% endhint %}

ContextQA embeds AI across the entire testing lifecycle — from generating test cases out of requirements to executing them autonomously in real browsers to healing them when the UI changes. Rather than bolting AI onto an existing tool, ContextQA treats the AI agent as the primary test executor: it reads your instructions, navigates your application, captures evidence, and reports results.

***

## What AI brings to your testing workflow

**Faster test creation.** Generate complete test cases from Jira tickets, URLs, Figma designs, OpenAPI specs, video recordings, plain text descriptions, code changes, and more — over ten supported sources. Instead of manually scripting every step, describe what you want to test and let the AI produce the steps.

**Autonomous execution.** The AI agent runs a nine-stage pipeline for every test: planning the approach, navigating the application, interacting with elements, capturing evidence, and self-healing when something changes. Each stage operates independently so failures are isolated and recoverable.

**Application awareness.** The Knowledge Base and Context Graph give the AI agent deep understanding of your application — login flows, navigation patterns, MFA handling, and inter-page relationships. This context reduces false failures and makes the agent behave more like an experienced team member who already knows the product.

**Domain specialization.** Custom Agents let you encode domain-specific testing logic — Salesforce validation rules, healthcare compliance checks, financial calculation verification — into reusable AI instructions that apply across test cases.

***

## In this section

| Page                                                                   | What it covers                                                                                                                              |
| ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| [AI Assistant](/ai-features/ai-assistant.md)                           | Using the in-app AI Assistant to navigate the platform, generate test cases from natural language, and get contextual help during execution |
| [AI Test Generation](/ai-features/ai-test-generation.md)               | Generating test cases from 10+ sources: Jira tickets, URLs, Figma designs, OpenAPI specs, video recordings, plain text, code changes        |
| [Autonomous Agent Pipeline](/ai-features/autonomous-agent-pipeline.md) | The nine-stage AI pipeline that plans, navigates, interacts, captures evidence, and self-heals during execution                             |
| [Knowledge Base](/ai-features/knowledge-base.md)                       | Uploading application context (login flows, navigation patterns, MFA handling) that the AI agent uses during test runs                      |
| [Custom Agents](/ai-features/custom-agents.md)                         | Creating specialized AI agents with domain-specific instructions for complex testing scenarios                                              |
| [AI Context Graph](/ai-features/context-graph.md)                      | The AI-powered context graph that maps relationships across your application for smarter test planning                                      |
| [Interactive Demo](/ai-features/demo.md)                               | Hands-on demo of ContextQA's AI capabilities                                                                                                |

***

## How AI fits into the platform

1. **Generate** — Create test cases from requirements, designs, or existing application pages using [AI Test Generation](/ai-features/ai-test-generation.md).
2. **Enrich** — Add application context through the [Knowledge Base](/ai-features/knowledge-base.md) and [Context Graph](/ai-features/context-graph.md) so the AI agent understands your product.
3. **Specialize** — Build [Custom Agents](/ai-features/custom-agents.md) for domain-specific testing logic that applies across your suite.
4. **Execute** — The [Autonomous Agent Pipeline](/ai-features/autonomous-agent-pipeline.md) runs your tests with planning, navigation, interaction, evidence capture, and self-healing built in.
5. **Heal** — When UI elements change between releases, [AI Self-Healing](/web-testing/self-healing.md) finds the equivalent new element and continues the test without manual intervention.

***

## Related pages

* [Core Concepts](/getting-started/core-concepts.md) — foundational platform concepts including how ContextQA's AI engine works
* [AI Self-Healing](/web-testing/self-healing.md) — detailed guide to self-healing behavior during web test execution
* [MCP Server](/mcp-server/overview.md) — programmatic access to AI features through the Model Context Protocol server

{% hint style="info" %}
**Generate tests from Jira tickets, Figma, and more — in minutes** [**Book a Demo →**](https://contextqa.com/book-a-demo/)
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://learning.contextqa.com/ai-features/ai-features.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.