Systems | Development | Analytics | API | Testing

The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.

Modern performance testing is evolving beyond the traditional choice between enterprise platforms and open-source tools. Teams increasingly need the flexibility of JMeter, k6, Gatling, or Locust combined with enterprise-grade reporting, scalability, security, and support. A new generation of platforms helps reduce operational complexity, lower total cost of ownership, and accelerate adoption through AI-assisted workflows and simplified onboarding.

This has been the reality of clinical decision-making for years: healthcare reacts after the signal becomes visible. Traditional clinical decision support systems helped standardize care and reduce errors, but most systems relied on static rules and issued alerts only after an event had occurred. They identify danger when it is already happening, not when it is quietly forming underneath the surface. That delay is expensive clinically, operationally, and financially.

AI agents do not just respond. They interpret goals, use tools, follow workflows, apply context, and sometimes change real systems. That means they need a different kind of 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.

New Sauce Labs Real Device Cloud Agent — available now in the watsonx Orchestrate Agent Catalog — can enable enterprise teams to trigger real-device tests, manage fleets, and validate app quality using natural-language commands.

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.