QA Wolf

Gartner predicts a 2,500% increase in software defects by 2028 if teams don't solve agentic verification. We automated code generation but have yet to automate testing. Agents ship 5-10 PRs a day. Your test suite? Still written by humans. PRs often go live largely untested. The numbers are terrifying: - AI-written PRs have an average of 11 bugs - Agents ship 20% more PRs YoY - Incidents per PR jumped 23.5% - Change failure rates climbed ~30% Your QA infrastructure is collapsing under agent-speed development. At QA Wolf, we've run 100M tests for companies like DoorDash, Drata, and Grafana. We've seen where traditional QA breaks in agentic development—and we've built a platform designed from the ground up to solve it. Agent-native testing. Purpose-built for speed and scale. The verification gap won't close itself. But it can be solved. Read our analysis (link in comments): Why Testing is Broken in the Agentic SDLC

AI made building internal dev tools feel like the obvious call. You've got a Claude subscription and a few afternoons — why pay for a tool when you can have something running by Thursday? Here's what teams keep getting wrong: the cost to start building dropped. The cost to own something didn't. There's a pattern nobody talks about: A team decides to build. The prototype looks great. Six months later, that same team is has abandoned the tool and is looking for vendors. Four illusions are driving most bad build decisions right now: --> The AI confidence effect — The demo works and it feels like the hard part is done. It isn't. --> The customization illusion — Most teams believe their requirements are more unique than they actually are. --> The cost illusion — Teams calculate the sprint cost. Not the debugging sessions, the upstream integration breaks, the opportunity cost of every sprint pulled away from actual product work. --> The "just a wrapper" assumption — The tool you're about to rebuild was built by a team of engineers and ML specialists who spent months on eval frameworks, prompt engineering, and latency optimization. Knowing when NOT to build is a superpower. Buy the tool. Point your builders at the problems only your team can solve. Link in comments!

We're proud to share that we've been named to the Inc. Best Workplaces 2026 list. A great workplace isn't built through perks or slogans. It's built by talented people who are committed to doing meaningful work, supporting one another, and continuously improving how they collaborate. This recognition reflects the culture our team has helped create through their feedback, trust, accountability, and shared commitment to our mission. We're grateful to every employee who contributes to making this a place where people can do their best work, grow in their careers, and have a real impact. Thank you to our team for making this recognition possible. We're excited about what we'll continue to build together. #IncBestWorkplaces

TFW one of your customers launches something amazing that you helped them perfect. 🥰 QA Wolf helped Brilliant: ⏩ Reduce QA cycles from 24 hours to 5 minutes. ⏩ Up their testing capacity by the equivalent of 4–5 additional QA engineers. ⏩ Detect 8–10 bugs per month saving millions of dollars in top-line revenue Read our case study with Brilliant below -- link in comments!

Unpopular opinion: AI coding tools might be creating more -10x engineers than 10x ones. AI is making everyone faster. But faster at what? AI-generated code has 1.7x more bugs than human-written code. So, when your team is shipping at 2x the velocity, they might also be shipping at 2x the risk. Enter the -10x engineer. They're not the one who can't keep up. They're the one moving the fastest. Shipping the most. Right up until they cause a site outage that costs millions. Or refactor the auth system on a Friday and take down login for half your users. The original 10x engineer was exceptional because of judgment. Knowing which shortcuts were landmines. Knowing when to slow down. Knowing what to throw away entirely. AI can 10x your output. It cannot 10x your judgment. Learn how to protect your codebase from -10x engineers in our latest piece. Link in the comments.

Everyone's talking about how AI is creating 10x engineers (or if you’re the founder of ClickUp, 100x engineers). Nobody's talking about the -10x engineers it’s also creating. They’re the type who loves coding with AI but doesn’t love the verification part as much. They don't break one thing. They break things at scale, at speed, and across systems. It’s the engineer who refactors the auth system on a Friday and takes the site down. Or the one who makes changes to a major service that breaks a dependency a week later. We started calling this the -10x engineer at QA Wolf. And they're the completely foreseeable consequence of how the industry adopted AI for coding. The math is simple: AI-generated code has 1.7x more issues than human-written code. And the volume of code AI makes possible outpaces the capacity of our normal verification processes. If your team is adopting AI coding tools without also: → Raising the bar on what "reviewed" actually means → Running comprehensive automated E2E tests that can block releases → Using feature flags and canary deployments to limit blast radius → Teaching engineers how to interrogate AI output, not just use it ...you're not creating 10x engineers. You're often just making mistakes faster. Learn more in our latest piece about how to spot -10x engineers and protect your team from them. https://lnkd.in/geePU3vM

They're not angry. They're just disappointed.

Code reviews are broken. And that's fueling a verification crisis. Here's what's happened to the average PR because of AI coding agents: → 70% more issues per PR → 154% longer diffs → 91% longer review times Same number of human reviewers. Same cognitive limits. The result? A 23.5% increase in incidents per PR and a 30% jump in change failure rates. AI code review tools help — but even the best ones miss 50% of bugs, according to independent benchmarks. And the bugs AI generates most often aren't likely to be caught in a review: → Integration failures → Silent data loss → Multi-step workflows that pass review and break in prod The fix isn't a better review process. It's more layers of verification. Like automated end-to-end testing that runs on every PR, covers every critical user flow, and blocks broken releases before they ship. We write about the crisis in the link in the comments and the need for more quality gates that scale with the speed of AI development.

QA Wolf now lets you feed any image, video, or audio file directly into the camera or microphone on real iPhones and iPads. So if your app does any of this: - Scans a receipt, then converts it to a PDF or extracts text with OCR - Identifies a song playing on the radio - Records and posts a video on social media - Scans barcodes and QR codes  - Hosts a video chat and validates call quality …you can test it the way your users will actually experience it. Here’s a look at Cameo using our video injection technology to test their iOS app. If you’re missing coverage for your complex workflows, schedule a demo to see our agentic testing platform in action.

QA Wolf now supports VPN configuration for iOS app testing. If your app serves different content by region, or your backend lives behind a private network, you've always had to work around your testing platform to cover those cases. Now you don't. VPN sessions spin up automatically at the start of each test run and tear down when it's done—no manual setup, no leftover state, no extra overhead when you want to run your suite. Come schedule a demo of our agentic testing platform, including 100% parallel run infrastructure for iOS apps, Android apps, and web apps. Read more about it here: https://lnkd.in/eGVbCrBp