Systems | Development | Analytics | API | Testing

Quick overview: Oracle’s Fusion quarterly update cycle has always been a pressure test for QA teams, but agentic AI automated testing may be changing that. Self-healing tests, natural language test creation, and context-aware agents are giving teams new ways to absorb Oracle’s pace of change without the usual scramble. As Oracle’s own AI capabilities make each release more complex, the tools designed to test AI-driven outcomes will matter more.

Every QA tester knows: time is money. When something breaks on your website or web application, it can cause major issues within minutes. One way to catch those problems early is smoke testing. Smoke testing answers one practical question before your team sinks time into deeper QA: is this build stable enough to keep testing? Instead of checking every detail, a smoke test focuses on the core workflows that need to work first.

Is agentic AI testing more hype than substance? Clarifying what this category of software testing actually is, how it works in practice, and where it still falls short.

In February, two CNBC journalists built a replica of Monday.com’s interface using Claude Code with no prior developer experience and less than $20 in credits. Software stocks at the time were already wobbly, with investors fearing that AI could erode seat-based pricing and undercut proprietary UI. To some, the CNBC news confirmed the speculation.

In this guest post, Intellyx Principal Analyst Jason English explores what it takes to level up quality engineering in the age of agentic AI, and why visibility, context, and governance are the keys to getting there. One day in an agentic developer’s life: Developer “CodeBud agent, create me a suite of test cases to validate the feature you just built.” CodeBud Done. Test suite created.

Performance testing is a critical safeguard for any software team, but even experienced practitioners can fall into familiar traps. Overlooked bottlenecks, missing test scenarios, or environments that don’t reflect production realities can all lead to slowdowns, user frustration, and lost business. The most damaging mistakes are often the ones that become invisible through routine or assumption.

If you run a platform tools or security team, you have likely heard this request from developers: “I just need a copy of the production database for staging so I can run realistic load and integration tests.” It is a completely reasonable request. Production traffic and data contain the actual request shapes, real-world value distributions, long-tail anomalies, and timing patterns that make tests useful.