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Are you using AI in testing?Great, this article is for you.If you’ve just spent three months learning AI testing, this article is for you. If your company just spent the budget on AI tools and you’re still seeing production bugs, this is for you.Here is why:The industry wants  speed. Faster test generation. Faster automation. Faster execution. Faster Go-To-Market (GTM).But here is what twenty years in testing has taught me: speed was never the problem.Strategy was.AI is making us test faster. But faster bad testing is still bad testing.We are optimizing the wrong thingMost testers I mentor do not have a testing speed problem. They have a "we don't know what to test" problem.They have a "We use AI agents to test. Still have production bugs" problem. They optimize for coverage metrics instead of business risk.AI does not fix this (at least as of now).Without proper human oversight, it may amplify the problem.I recently spoke with a company leader who mentioned they automated hundreds of

In the previous chapter, I introduced the 5W1H AI agent framework and explored What AI agents are, How they work in testing contexts, and Why they matter.Now, we will dive deeper into this framework to understand When to use them, Where they fit, and Who provides agentic capabilities.When to Use Which Level of Agent?Agents come in various levels.  Each level solves a different type of problem. Some follow fixed rules. Some help you think. Some run tasks on their own. You need to select the level that fits your workload, risk, and control needs. This section will break down what each level does and when you should use it.Level 1: Rule-Based Systems (Traditional Agents)These are traditional automation agents that operate on predefined rules and logic. You can also call them as “Pre-LLM era” agents or traditional agents. Built using: Programmable rules. Fixed conditions. If-else logic. Behavior:Executes tasks exactly as programmed No reasoning capabilities (as they are not built using LLM

🎓 Course Overview: Generative AI for TestersGenerative AI is revolutionizing software testing, transforming it from a futuristic concept into a present-day necessity. To equip testers with the skills needed to navigate this evolving landscape, Tricentis presents the Generative AI for Testers course. This interactive mini-course is designed to help testers understand, apply, and benefit from Generative AI in their testing processes.📁Structure of the course: Introduction to Generative AI for Testers Deep Dive Leveraging LLMs and Mastering Prompt Engineering Implementation Strategies and What Comes Next🧠 What You'll LearnBy the end of this course, you will be able to: Explain the fundamentals of Generative AI, how it differs from traditional AI, and its role in software testing. Identify and apply AI-powered testing techniques, including prompt engineering, in-context learning, and model selection. Evaluate the advantages, limitations, and biases of Generative AI models to make inf

I bet you all like free courses!We’re cooking up some Udemy-style exclusive courses (totally free) just for the community.We want you to define what they’ll cover.👉 What topics would actually be useful for you? Drop your ideas below — we’ll build the stuff you really want to learn.

The AI wave hits software testing2024 was a wild ride for anyone working in tech. AI seemed to explode overnight, and suddenly everyone was talking about how it would transform software development and testing. The Model Context Protocol (MCP) came out in late 2024, and that really accelerated things, it standardized how AI models can connect to external tools and data sources, making AI a lot more practical for day-to-day work.What struck me most was how different this felt from earlier waves of core Machine Learning and Deep Learning. Back then, you needed a strong background in statistics and mathematics to get something useful of it. Now? Pretty much anyone can spin up an AI assistant and get real work done. I have seen product managers, junior engineers, and even folks from completely non-technical backgrounds successfully using AI to solve problems that would have required specialized skills just a couple of years ago.2025 became the year where we all stopped playing around and s

We are thrilled to announce that ​@ujjwal.kumar.singh has been selected as the ShiftSync User of the Month for January 2026!Why ​@ujjwal.kumar.singh ?Ujjwal has consistently demonstrated exceptional engagement and contribution to the ShiftSync community. Whether helping other members, sharing insightful tips, or providing valuable feedback, Ujjwal has been an invaluable part of our platform.Ujjwal’s sustained enthusiasm and expertise have made a significant impact on our community culture and collective knowledge.Highlights of Ujjwal's Contributions:Active participation in community discussions and solution sharing. Providing thoughtful and constructive feedback on feature updates. Leading webinars and helping new users get acclimated with the platform. Contributing insightful articles and how-to guides on performance engineering and system optimization.As User of the Month, @ujjwal will receive a special badge on their profile, a shareable certificate, and a customized ShiftSync swag

Do not prepare for interviews.Prepare for the job. Prepare the value you will add. If you are still preparing for interviews in 2026, you are already behind.Here is why:AI can coach anyone on interview questions now. Online courses teach you tools in 30 days. Anyone can be trained to "answer well." But here is what twenty years in the testing industry has taught me: interviewers don't hire you for good answers. They hire for your proof of impact and potential.They want to see what you have actually delivered. What have you improved? What have you learned and applied? What value did you add?That's what stands out. That’s what hiring managers like me expect.The Smartest Preparation Starts Before You Need a JobMost testers apply for roles first. Then they try to fit themselves into the requirements.Actually, it’s backwards.Self-awareness:Before you apply for a position, do a personal SWOT analysis.Know your actual strengths - not the ones you think employers want to hear. Understand your

What it is:Instead of spending hours piecing together CI logs, flaky failures, and requirement coverage for release meetings, use AI to auto‑compile a short Release Risk Brief from your automated results and requirements links. Think of it as a tidy, human‑readable snapshot: what changed, what failed (and why), what’s at risk, and what to do next.It’s platform‑agnostic, but plugs in neatly if your automation (e.g., Tosca or Testim) reports into Tricentis qTest/Insights. Why now (trend‑aligned, not hype):AI is moving from “test generator” to decision support in QA—helping teams prioritize, summarize, and make release calls faster.  The latest qTest Copilot and Insights updates emphasize AI‑assisted authoring and unified reporting—exactly the signals a Release Risk Brief needs.  Tools increasingly cluster and classify failures with ML, cutting triage noise so your brief focuses on real defects vs. environment or flaky tests.  Tricentis’ agentic AI direction (MCP servers, agentic test aut

🎉 Announcing Our User of the Month for December 2025: ​​@سامان ذوالفقاریان (Saman) We are delighted to name ​@سامان ذوالفقاریان  as our User of the Month for November 2025! 🏆​Saman has consistently excelled in our community, offering invaluable insights, well-thought-out solutions, and unwavering support to fellow members. His dedication and expertise have significantly enriched our knowledge base and aided many in overcoming challenges.Why ​Saman Stands Out:📝 Exceptional Contributions: Authored insightful and practical posts with valuable tips and advice. 🤝 Community Spirit: Always ready to help, fostering a sense of collaboration and teamwork. 👨‍🏫 Expertise: Demonstrates deep technical understanding, guiding users through complex scenarios effortlessly. 🚀 Innovation: Introduced innovative ideas and methodologies that benefit the entire community.As a token of our appreciation, ​Saman will receive:A special gift box with exclusive goodies (or a giftcard). A badge to display on

MCP is an open standard that lets AI assistants (like Claude, Copilot, Cursor) securely “see” and act on your NeoLoad Web data and APIs—so you can ask natural‑language questions, launch runs, compare results, and even pull in CI/CD and APM context without custom glue code. NeoLoad’s MCP server is available in the next‑gen NeoLoad Web experience, with setup driven by an API token and role‑based access.What is MCP? and why performance engineers should care?Model Context Protocol (MCP) is an open, vendor‑neutral standard for connecting AI assistants to tools and data. Instead of one‑off, model‑specific integrations, MCP defines a universal way for AI agents to discover tools, authenticate, and call functions exposed by a server (for example, the NeoLoad Web API). Practically, this means your assistant can list workspaces, start tests, fetch metrics, and compose findings across systems.The standard has gathered momentum across the ecosystem (IDEs, cloud platforms, enterprise tools), making

This happened a few years ago, when I was working as an SDET in a product company. At the time, it felt like just another sprint. Looking back now, it became one of those moments that reshaped how I test.It taught me that users don’t read acceptance criteria, and that testing isn’t only about validating what’s written — it’s about predicting how real people will behave once the feature is live. They click around, skip steps, and try things we never imagine during sprint planning.We were an agile team working on a transport and tourism e‑commerce platform based in London. This particular sprint focused on improving the customer journey, and one of the key deliverables was a new user‑details form that appeared during the purchase flow.Some fields — like Full Name, Email Address, and Payment Details — were mandatory for checkout, while others — such as Alternate Phone Number, Special Requests, or Promo Code — were optional.Our goal was to ensure the entire end‑to‑end experience worked smo

From “cool demo” to actionable playbook.Here’s what the community asked, and what you can put into practice right away.In the webinar, Nikolay showcased a multi‑agent PR review system (code quality, test quality, security, prioritization) that analyzes every GitHub PR/commit and pushes results to a live dashboard. It was stitched together with v0/Vercel for the UI, Claude Code for implementation, GitHub Actions for CI, CodeRabbit & GitHub Copilot for reviews, and Temporal for orchestration.TL;DR (What you’ll learn in this post)How to keep tests stable when AI suggestions are nondeterministic (without over‑mocking) How testing evolves when most of the app is AI‑generated (think contracts + runtime monitoring + evals) The four metrics that keep reliability from quietly degrading as shipping speed accelerates. A pragmatic way to triage AI‑amplified PR noise in open source and internal repos. Why Temporal‑style orchestration is the hidden superpower for durable, long‑lived agent workfl

Model Context Protocol (MCP) is an open standard that lets AI assistants (and agentic tools) talk to enterprise systems through a consistent, secure interface—think of it like USB‑C for AI tools. In practice, MCP gives an AI chat/agent access to resources, prompts, and tools exposed by a server, over a JSON‑RPC connection, in a way that’s LLM‑agnostic and designed with explicit user consent and controls.Why it matters for quality engineering: Tricentis now ships a qTest MCP server that bridges AI clients (e.g., Cursor, Claude) with the qTest API so you can converse your way through common test‑management work: list requirements, inspect linked tests, spot gaps, create missing test cases, analyze executions, and even file defects—all without writing glue scripts or clicking through deep UI menus. How the qTest–MCP stack fits togetherMCP client (e.g., Cursor): The IDE/assistant that speaks MCP and can attach servers via local mcp.json or remote transport. Cursor documents first‑class sup

The Challenge of Salesforce TestingSalesforce is a powerful but complex platform, and testing it can sometimes feel like finding your way through a labyrinth. QA engineers often face tedious, repetitive test scenarios that must cover countless combinations of objects, fields, and user permissions. Manually clicking through each scenario (or writing long Selenium scripts) isn’t just time-consuming; it’s also prone to human error and difficult to maintain. Every time Salesforce updates or a business process changes, testers may spend hours updating scripts or re-running manual tests to ensure nothing broke.Sound familiar? If you’ve ever had to verify every picklist value for every user role, or retest a 100-step flow after a minor change, you know the pain. Even traditional automation can require a whole team of experienced coders building and fixing scripts. It’s enough to make any tester reach for a third cup of coffee! ☕️But what if testing Salesforce could be smarter, faster, and (da

MCP Certification course by Karthik K.K. COURSE INTROCourse description:This video series introduces QA professionals to the Model Context Protocol (MCP) — a powerful way to connect testing tools, data sources, and automation workflows with AI-driven efficiency. Across ten practical sessions, you’ll learn how MCP simplifies integration challenges, streamlines test execution, enhances reporting, and makes QA assets — from databases to UI tests — more accessible and maintainable. Whether you’re looking to modernize your existing frameworks or prepare for the future of AI-assisted testing, this course gives you the knowledge and hands-on skills to bring MCP into your QA practice.📁Structure of the course: Introduction Installing MCP Server UI Testing With Playwright MCP Server UI Testing VS Code CodeGen API Testing With MCP Server DB Testing With MCP Server File System MCP Server BDD Test Case Agent Test Management MCP Server Future State of MCP Server​​​​​✅CertificationComplete all chapt

One of the biggest hurdles when testing AI-powered features is the oracle problem—it’s hard to state what the “expected result” should be, especially for language outputs that vary from run to run. Formal guidance for testing AI systems even calls this out explicitly.The tip: whenever your product uses an LLM, force its response into a strict JSON Schema and assert on fields—just like any other API. OpenAI, for example, supports Structured Outputs that guarantee responses conform to a schema; Microsoft’s Semantic Kernel shows the same approach in app code. This turns fuzzy text into deterministic fields your tests can validate.Why this works (and what to watch for)From prose to contracts. A JSON Schema acts as a “contract” (types, enums, required fields). When the model adheres to your schema, your test can assert on booleans, enums, and numbers—not on paragraphs. Vendor‑agnostic pattern. Independent evaluations show multiple vendors can produce structured JSON at scale, though APIs di

In the ever-evolving world of software testing, AI is opening up transformative possibilities for QA teams. Tricentis has leveraged its years of AI innovation (from Vision AI to self-healing tests) to introduce Agentic Test Automation – an AI-powered “co-worker” embedded in Tricentis Tosca that can autonomously generate and execute test cases from natural language descriptions. This advanced capability is poised to revolutionize how QA engineers and software testers design automated tests, addressing modern challenges of scale and complexity in software delivery.Modern Testing Challenges and the Need for AI AssistanceDevelopment cycles are accelerating and codebases are growing larger than ever. With the advent of generative coding tools, developers can produce code at unprecedented speed – but this leaves QA teams scrambling to test more functionality in less time. Moreover, AI-generated code can introduce unexpected issues and variations, demanding smarter testing strategies. Traditi