Systems | Development | Analytics | API | Testing

AI Coding Tools and API Governance: Here's Why You Need Both.

GitHub Copilot, Claude, and Cursor have become genuine superpowers for API development. They draft OpenAPI definitions, generate endpoints, propose schema changes, and write test cases — all from inside the IDE, in real time. Teams using these tools are generating API definitions faster than most thought possible even a few years ago. That velocity is real, and it’s reshaping how engineering teams think about their toolchain.

Get Started With LLM Proxy in WSO2 API Platform AI Gateway

Run your first LLM proxy on WSO2 Platform AI Gateway in minutes — no cloud setup required, just Docker. This quickstart walks you through spinning up the WSO2 Platform AI Gateway as a standalone component on your own infrastructure. You'll add an OpenAI provider configuration (including API key auth and access control rules), deploy an LLM proxy that routes through it, and verify live responses end to end. What you'll set up.

Four signs your automation suite is costing you more than it's saving

An automation suite that’s losing ground rarely makes it obvious. Coverage numbers look reasonable. Tests are running. The CI pipeline is green more often than not. Meanwhile, the team is quietly working around what isn’t working – rerunning tests until they pass, deferring maintenance, or accepting a regression window that’s wider than it should be. Those workarounds can feel normal. They aren’t.

Why your automated UI tests keep breaking

Automated test suites tend to follow the same arc. The suite works well until the application changes and a block of tests fails. Someone fixes them. The application changes again. At some point, the work of keeping tests current starts consuming the time that should go toward coverage decisions, risk assessment, and the testing work that requires human judgment.

Automated testing vs. autonomous testing

Autonomous testing is one of the most talked about developments in software quality right now. It shows up in analyst reports, vendor pitches, conference talks, and job descriptions – often in the same breath as automated testing. Most of those conversations treat the two as interchangeable, or worse, position autonomous testing as simply a smarter, more advanced version of what teams already do.

Build resilient end-to-end tests with AI agents in SmartBear Reflect | Demo Den

See how SmartBear Reflect uses agentic AI to build end-to-end tests in minutes and keep them resilient as your application changes. In under 20 minutes, Reflect co-creator, and SmartBear Director of Product Management, Todd McNeil walks through live test creation across web and mobile, with zero fluff.

Digital Twins for Devs & AI Agents - Record, Replay & Catch Regressions | Keploy

Give your developers — and your AI agents — a digital twin of your live environment. Keploy records real traffic from your live services (no production access, nothing to spin up) and replays it as a faithful twin, so you can continuously verify behavior and catch regressions before they ship. In this demo: record a live service, turn that traffic into integration tests and mocks automatically, replay everything against digital-twin sandboxes, and wire it into CI for continuous verification.

Mobile testing, reimagined: How Reflect's Mobile Testing Changes QA

Mobile application users expect flawless experiences on every device, every OS version, and every screen size, and they have little patience for anything less. Yet for QA teams, achieving that level of coverage traditionally means wrestling with brittle automation scripts, complex Appium setups, and endless device fragmentation. Even after all this manual effort, your mobile app quality could contain unseen gaps.