Product

While AI coding assistants accelerate feature development, code-level assertions frequently miss visual UI regressions. This article by Khoi Le explains how the Applitools Model Context Protocol (MCP) server integrates deterministic Visual AI into IDE tools like Copilot and Cursor. By combining computer vision with existing developer workflows, engineering teams can eliminate test maintenance and receive reproducible visual release signals inside their PRs.

Test PDFs, skip false positives with Dynamic Match Level, and triage visual diffs in plain English. See what’s new in Applitools.

Legacy post-production monitoring relies on high-maintenance developer scripts or inaccurate probabilistic AI. This post explores how Applitools Autonomous uses Deterministic Language Models (DLM) and Visual AI to deliver codeless, locator-free visual checks—protecting revenue and catching silent defects in minutes with zero false alarms.

Learn how to set up the Applitools ServiceNow integration to automate license and usage data retrieval via REST API for procurement teams.

TL;DR To automate Applitools baseline branch merging within CI/CD pipelines, use a Shared Library to call the Applitools Eyes Server API. This eliminates manual baseline maintenance in the Eyes Dashboard,…

The Regions Only match level solves the “noisy neighbor” problem by isolating assertions for precision at scale with no false positives.

Learn how Autonomous eliminates local path dependencies and validates post-upload UI state with cloud-managed file upload test automation.

The Applitools Eyes MCP server connects your AI assistant to visual testing. Set up Eyes, add visual checkpoints, and catch regressions before they merge.

The Agentic SDLC shifts the bottleneck from code production to verification. Learn how Applitools provides a deterministic quality gate with Eyes MCP and Autonomous Troubleshooting to fix the “flakiness loop” and reduce test maintenance costs.

How do you integrate Applitools with PractiTest?
You can integrate Applitools Autonomous with PractiTest by building an event-driven serverless bridge using AWS Lambda and DynamoDB. This workflow uses PractiTest webhooks to trigger Applitools Visual AI tests, stores session IDs in DynamoDB, and automatically pushes visual validation results back to the PractiTest dashboard via REST API.

Visual testing in Playwright often forces teams to choose between strict failures, snapshot maintenance, and CI pipeline complexity. This article explores how a single configuration flag introduces three different strategies for handling visual differences and improving the Playwright developer experience.

Test Storybook components with Visual AI inside Storybook. Catch UI bugs early, bulk-maintain baselines, and scale cross-browser coverage.