Systems | Development | Analytics | API | Testing

AI is Exposing Observability's Dirty Secret

The 3 pillars of observability are breaking. For years, dev teams relied on Logs, Metrics, and Traces to know when something went wrong. But now? AI agents are writing, deploying, and changing code in real-time. When an AI hallucination pushes a bug to production, standard monitoring sees nothing wrong.To survive the AI era, we need a 4th Pillar of Observability. Watch to find out what it is and why the old way of monitoring just became obsolete.
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Build WireMock mappings fast from real traffic

I'm a big fan of service mocking. I've been working in and around software for about 25 years, and one thing never changes: when you sit down to work on your code, you almost never have everything available. The database, the third-party API, the message queue, the service two teams over. Something's missing. So you've got to stub it out or mock it out and keep moving.

Enterprise test management: Should you build or buy in the age of AI?

AI has opened the door for teams to build tools they previously had to buy. With the right prompts and internal workflows, teams can generate test cases, summarize results, analyze defects, and automate parts of the testing process faster than ever. For enterprise QA and engineering leaders, that raises a practical question: “should we build our own test management layer, or adopt an AI-powered test management platform?” It’s a fair conversation to have.

AI writes code in seconds, but delivery still takes days

The pitch for AI coding was speed. Claude Code, Copilot, Cursor, whatever you’re running, they all generate business logic faster than you can review it. That part is real. But look at what happens after the code gets written and the numbers get ugly. CircleCI’s 2026 State of Software Delivery Report found AI drove a 59% increase in average throughput.

How to Trace Microservices WITHOUT OpenTelemetry

Learn how to trace complex multi-hop transactions across microservices when OpenTelemetry isn't available or trace parents get lost. This quick tutorial demonstrates how to use full-text search in proxymock (a free CLI and UI tool) to isolate user journeys (like looking up a specific username) and instantly generate waterfall tracing views. Whether you are working locally or in a Kubernetes cluster, you can record API traffic, visualize multi-hop requests, and easily transition those captures into load or regression tests.

Ship production-ready APIs faster and confidently with SmartBear ReadyAPI | Demo Den

API testing gets harder as it grows: fragmented tools, brittle scripts, thousands of endpoints, and external dependencies that block progress. SmartBear ReadyAPI lets teams ship production-ready APIs faster by validating functionality, performance, and security in one test, with virtualization built in – no separate tools to juggle. In this Demo Den, Thomas Hurley, senior manager of product management at SmartBear, shows how one test does the work of three, so teams cut maintenance time and stop waiting on external dependencies.