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
Category: Agentic QA
Agentic QA is the next evolution of software testing — where autonomous AI agents read user stories and requirements, generate test cases across manual, exploratory, BDD, and automated testing workflows, self-heal broken selectors, and maintain full coverage without human intervention at every step. This category covers the architecture, tooling, and real-world implementation of Agentic QA — from Plan-Act-Verify reasoning loops and LLM-based visual regression to intelligent test prioritization, HITL validation, and native CI/CD pipeline integration. Whether your team runs Gherkin scenarios, Playwright suites, unit tests, or structured manual cycles, this is where autonomous AI changes how testing gets done.
Explore how TestStory.ai Agent and TestQuality power the shift from reactive test authoring to outcome-driven quality engineering.
Generative AI for QA: How SDET Workflows and Skills Are Changing
At a Glance Generative AI for QA: Where Generation Ends and Orchestration Begins The real shift is not better prompts. It is better workflow design. The verification gap: According to the Stack Overflow 2025 Developer Survey, 45.2% of developers now spend more time debugging AI-generated code than writing it manually — workflows have shifted from… Continue reading Generative AI for QA: How SDET Workflows and Skills Are Changing
Human in the Loop Testing: Where AI Ends and QA Judgment Begins
At a Glance Human in the Loop Testing: Where AI Ends and QA Judgment Begins The question isn’t whether to use AI in QA. It’s knowing exactly where to keep a human in control. The core risk: Over 75% of multi-agent failures are silent semantic errors that pass automated checks but violate business logic —… Continue reading Human in the Loop Testing: Where AI Ends and QA Judgment Begins
Is Pi Coding Agent Fast Enough for Agentic QA? A Qwen3.6 MTP Benchmark
Pi Coding Agent is a minimal terminal coding harness built by Earendil Inc. that gives large language models direct read, write, edit, and bash access to a local codebase. It runs locally, supports Anthropic, OpenAI, and local model providers, and is designed to be extended through TypeScript extensions and skills. For QA teams evaluating local… Continue reading Is Pi Coding Agent Fast Enough for Agentic QA? A Qwen3.6 MTP Benchmark
How to Stop Bugs from Slowing Down Software Releases
Defect management is the end-to-end process of capturing, triaging, routing, retesting, and closing software defects before they block a release. Most teams discover bugs fast enough — the delay comes in everything that happens after discovery: chasing reproduction details, clarifying which environment is affected, and confirming whether a fix actually holds before shipment. A fragmented… Continue reading How to Stop Bugs from Slowing Down Software Releases
How to Test MCP Servers with DeepEval
MCP server testing is the practice of validating that a Model Context Protocol server exposes the right tools, passes the right context, preserves session state across turns, and returns outputs an LLM can use correctly in real agentic workflows. For QA teams building AI products, this means testing not just API responses but complete tool-driven… Continue reading How to Test MCP Servers with DeepEval
Why Gemma 4 QAT Struggles in Local Coding Agent Tasks
Gemma 4 QAT refers to Google’s quantization-aware versions of Gemma 4, designed to reduce memory use and improve local inference speed on developer machines. In a direct head-to-head coding-agent task using VS Code and DeepEval, Gemma 4 QAT produced structurally incomplete test code — initializing evaluation metrics without applying them correctly and omitting the required… Continue reading Why Gemma 4 QAT Struggles in Local Coding Agent Tasks
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
Agentic Testing and How QA Teams Can Use Claude Code and Terminal Agents
Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant… Continue reading Agentic Testing and How QA Teams Can Use Claude Code and Terminal Agents