Systems | Development | Analytics | API | Testing

Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant that understands repository context, proposes changes before applying them, and generates test assets across Playwright, Selenium, and API testing workflows.

Automated load testing has reached a turning point in 2026. Artificial intelligence, once a gradual addition, now drives a clear shift in how organizations validate performance. Industry reports project a 15% compound annual growth rate (CAGR) for AI in software testing from 2023 to 2026, underscoring the urgency to modernize testing practices and keep up with rapid development cycles.

State and local governments are under growing pressure to modernize the systems that power essential services, from healthcare and human services to transportation and public safety. At the same time, citizens expect fast, seamless digital experiences when interacting with government agencies. To meet these expectations, many agencies are investing in large-scale ERP transformations, including SAP and Oracle migrations.

We surveyed 2,501 IT decision-makers, QA professionals, and business leaders across six countries for our second annual Quality Transformation Report. Respondents came from organizations with 150-plus employees across manufacturing, energy and utilities, retail, financial services, and the public sector. One of the major findings: confidence in AI agents making release decisions dropped from 48% in 2025 to 34% in 2026. That’s a 14-point decline in a single year.

New offering enables enterprise customers to validate agentic workflows against a global network of skilled, real world testers before failures become customer, compliance, or brand crises.

A 3D mesh for an elevator button with millions of triangles was added to a AAA game. No one flagged it. Nothing caught it. By the time anyone noticed the effect on performance, the artist had moved on, the asset was buried under dependencies, and the team was three months from shipping.

Gherkin software testing turns plain-English specifications into executable tests your whole team can read, but only when you stop treating it like a scripting language. If your feature files read like step-by-step UI scripts, you're doing BDD testing backward. Here's how to fix that.

Here’s a problem I run into constantly: something breaks in production, I can see the 500 errors in my logs, but I can’t reproduce it locally. The trace shows me the dependency graph but not the actual request that failed. This is especially painful in microservices. I was looking at a CNCF example the other day (a simple demo app, like 4 pods) and it already had so many cross-service dependencies that understanding what broke required looking at the whole system at once.