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The Quality Trail: November 2025 QA News

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From the Desk of the Editor

Hey there, and welcome to another installment of the Quality Trail.

This month, though we’re running a bit delayed, we’ll be highlighting current trends and what’s likely ahead going into 2026, as well as providing ideas and proven AI workflows that you can use to become a more effective tester and leader! 

As always, let us know if you think we’ve missed something, or share the link with your colleagues or partners who may benefit from some or all of this information. You can also sign up to receive these testing updates via email.

– The QualityLogic Editorial Team

What’s Inside

Some may recall that at the end of 2024, the tech and AI space mutually determined that 2025 would turn out to be the year of Agentic AI (that is, GenAI that is trained on a specialized pool of knowledge, can contemplate problems, think about solutions, and ultimately use tools to execute actions on a user’s behalf with little to no input). Gartner predicted this would be the top strategic technological trend this year. Were they right? Realistically, that remains to be seen, but AI agents have indeed been all the rage. 

Per Gartner, some of the Top Strategic Technology Trends for 2026 include 

  • AI-Native Development Platforms: Software like Antigravity, Cursor, Windsurf, and Replit 
  • AI Supercomputing Platforms: Infrastructure that allows GenAI model providers to handle requests at scale. 
  • Multiagent Systems: Applications allowing modular AI agents to collaborate on tasks in parallel. 
  • Physical AI: Devices that bring AI into the physical world, from wearables to robotics and drones. 

With relevance to quality, we recently came across a post from talent 500 breaking down the major trends that have started to take off in 2025, and that are expected to continue throughout 2026: 

  • AI Agents are Becoming Active Testing Partners. Engineers are increasingly leaning on AI to help them write automated test cases, prioritize which ones run first, and isolate patterns. 
  • Shift-Right Testing Powered by AI Analysis. That is to say, testing which does not end at release. AI continuously monitors production, derives tests from anonymized user activity, and flags things that don’t look quite right as they happen (outside a sandboxed environment). 
  • Evolving Role of QA Engineers. Gone is the era of QA specialists working independently to perform manual tests and report on defects from the sidelines. These days, even entry level testers must possess expertise in automation, literacy with AI, and familiarity with DevOps pipelines to set themselves apart. They must also collaborate with stakeholders at all levels to ensure that testing is able to happen throughout the software development lifecycle and beyond. 
  • Playwright Gains Prominence as the Framework of Choice. Its speed, cross-browser support, built-in tracing, API testing, and integration with MCP (model context protocol) have really set this tool apart from the competition. Expect an increasing desire to migrate over old tests. 

What Are People Actually Using AI For?

In a world of hype, it’s a good question to ask. Someone on the Software Testing subreddit asked this exact thing a few days ago, which prompted us to come up with our own list of the quick wins that we have actually seen work. The important thing to keep in mind is that the majority of AI-backed tools out there will create more tech debt than they solve, at least for now. These ideas are meant to be model and mostly stack agnostic to get you thinking.  

Here is a short list: 

  • Use Chrome DevTools MCP, Playwright MCP, and Atlassian MCP to draft testing acceptance criteria and user stories. Once they have been validated by a human, they can be added to Jira. 
    • Additionally, someone suggested using the Playwright MCP server to test run an application and output potential issues/subtasks it finds to Jira under a dedicated epic. This workflow gets better and presents fewer false positives as you add more context. 
  • Fake data generation. GenAI is great at hallucinating, sometimes this limitation can help you. It will happily generate names, addresses, schemas, and more. 
  • Feed your test cases to AI, and ask whether there are any paths, user flows, preferences, or workflows that you aren’t accounting for. 
  • Convert unopinionated, tedious, and repetitive manual checks into automated tests, then run them until the automated results match your manual ones. 
  • Capture action items from meetings. Humans comment on them and how they wish to get the job done (adding all relevant details), then AI helps solidify a plan. Mileage will vary based on the complexity and documentation of the internal tools that are needed. 
  • Parse and explain long error logs, complex stack traces, or test failures. 
  • Preliminary accessibility test cases with axe MCP server. 
  • How I Used ChatGPT to Convert Cypress Tests into Playwright | by Connie Beaty | Nov, 2025 | Medium. A slightly different workflow is demonstrated is this video: ONE Prompt to Convert ENTIRE Selenium Test Suite to Playwright | Build This AI Agent Yourself! – YouTube 
  • AI for QA pipeline: From a user story to a verified test – Nick Gomanov (Medium) 

Do you know more? What’s working for you at your company? Let us know! 

What We’ve Been Reading

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