Agentic Testing and QA with Playwright and Cucumber

Playwright and Cucumber is a BDD testing combination that pairs Gherkin feature files with Playwright’s browser automation engine, letting teams write behavior in plain language while executing it through deterministic, code-driven checks. Cucumber parses Feature and Scenario statements written with Given, When, and Then keywords, step definitions bind those statements to TypeScript or JavaScript functions,… Continue reading Agentic Testing and QA with Playwright and Cucumber

Gauntlet Loop: How AI Agents Build, Judge, and Fix Work

A Gauntlet Loop is an agentic AI workflow in which a lead agent breaks a broad goal into small, independently judgeable pieces, assigns them to specialist builder agents, and routes every result through a separate judge agent that compares the work against a quality bar. The pattern was popularized by Matt Shumer’s July 2026 “Claude… Continue reading Gauntlet Loop: How AI Agents Build, Judge, and Fix Work

Building an Agentic Playwright Framework for QA Teams

An agentic Playwright framework is a Playwright test suite extended with AI agents that handle bounded work around execution: generating schema-controlled test data, analyzing failure evidence, and surfacing flaky-test patterns. Playwright still owns browser automation, assertions, and pass/fail reporting — agents sit beside that loop, not inside it. Each agent reads a defined input, such… Continue reading Building an Agentic Playwright Framework for QA Teams

How custom AI agents via MCP extend autonomous QA

Custom AI agents via MCP (Model Context Protocol) let an autonomous QA system reach beyond its built-in skills by connecting to external tools such as GitHub and browser automation services. In practice, that means a QA agent can inspect source code changes, identify new features, compare them against existing test coverage, and create missing test… Continue reading How custom AI agents via MCP extend autonomous QA

CLI Coding Agents for QA Engineers: Setup, Workflows, and Tradeoffs

At a Glance CLI Coding Agents for QA: What You Actually Get Terminal-resident, repo-aware, and capable of running your entire test loop autonomously. Scope advantage: CLI agents operate across your entire repository — not just open files — letting you assign multi-file refactors, coverage gap analysis, and bulk selector updates without leaving the terminal. Verification… Continue reading CLI Coding Agents for QA Engineers: Setup, Workflows, and Tradeoffs

Claude Code with Playwright MCP: Agentic AI Test Automation Setup Guide

Claude Code with Playwright MCP is an agentic QA workflow where Claude Code uses the Playwright Model Context Protocol server to connect a coding agent to a live browser. The agent navigates the application, reads the actual DOM, captures real selectors, and generates executable Playwright tests from what it observes — instead of guessing page… Continue reading Claude Code with Playwright MCP: Agentic AI Test Automation Setup Guide

How To Integrate Agentic Testing Into Your CI/CD Pipeline

At a Glance Agentic Testing in CI/CD: Where the Boundary Is and How to Cross It Cleanly AI drafts the tests. Playwright runs them. The CLI governs both. The boundary is strict: Agentic tools belong in the drafting layer — analysis, coverage planning, and script generation. Deterministic frameworks like Playwright or Selenium own execution. Mixing… Continue reading How To Integrate Agentic Testing Into Your CI/CD Pipeline

Free and Paid Ways to Run Cloud Code for Agentic Testing and QA

Agentic Testing and QA describes a testing workflow where an AI coding assistant does more than answer one-off prompts. It can inspect a project directory, reason over multiple files, propose test scaffolding, and work in a continuous loop with the engineer — rather than waiting to be prompted at each step. The practical bottleneck for… Continue reading Free and Paid Ways to Run Cloud Code for Agentic Testing and QA

Best Practices for Implementing Test Automation in CI/CD

Effective test automation in CI/CD pipelines blends strategy, smart tooling, and AI-assisted workflows to ship faster without breaking quality. Pick a strategy that matches your delivery velocity, then layer in AI-assisted test generation as your automation maturity grows. Software teams are shipping more code than ever, and a lot of that code is now written… Continue reading Best Practices for Implementing Test Automation in CI/CD

Continuous Testing Pipeline: Test Management for CI/CD

Key Takeaways Pipelines are ephemeral. Your test management layer shouldn’t be. A vendor-agnostic continuous testing pipeline is the only way to keep test history intact across CI/CD migrations. The delivery gap is widening: AI throughput is up 59% YoY, feature-branch activity is up 50%, but main-branch success has dropped to a five-year low of 70.8%.… Continue reading Continuous Testing Pipeline: Test Management for CI/CD