Systems | Development | Analytics | API | Testing

Teams use different tools in their software testing life cycle. The problem? Each tool has its own way of communicating. The MCP (Model Context Protocol) Server is a new approach to integrating these tools. It’s like a universal translator, so your testing tools, scripts, and AI copilots can share context without endless plugins or one-off integrations.

Traditional automation in software testing is beginning to show its limitations. Once regarded as the benchmark for speeding up QA, now struggles to keep pace with modern software development. Agile methodologies, DevOps practices, continuous delivery, and rapidly evolving user journeys require testing strategies that are more innovative, quicker, and adaptable.The challenge? Old automation frameworks still lean too much on people. They rely on fixed scripts, constant maintenance, and manual oversight.

As an application developer integrating analytics into your application, your users expect a scalable, flexible solution that adapts to changing business needs. While organizations strive to capitalize on new AI tools, they’re also still wrestling with big data: massive, fast-moving datasets that traditional tools can’t handle easily.

For years, QA practices were designed for predictable, rules-based software. AI has upended that reality by introducing risks that traditional methods cannot fully address.

Generative AI copilots are moving from experimental tools to core enterprise solutions. But too often, organizations rush into development, only to discover adoption stalls because the copilot doesn’t solve a specific user problem, lacks trust safeguards, or can’t scale reliably. This guide lays out best practices across the entire lifecycle, from planning and building, to deployment, monitoring, and long-term maintenance.

QA teams have never been more central to product success or more pressed for time. As complexity increases, testers are expected to deliver broader coverage and deeper insight into system behavior while keeping pace with shorter release cycles. Model-based and data-driven testing offer a structured way to design tests that uncover gaps, ensure coverage, and reduce duplication.

Analyzing performance test results has long been one of the most complex and time‑consuming aspects of performance engineering. As the volume of testing increases and delivery cycles continue to accelerate, teams face mounting challenges in making sense of vast amounts of data.

AI agents can manage a wider range of tasks than any automation tool yet developed, thanks to their decision intelligence and context reasoning capabilities. Agentic workflows, or processes where at least some of the work is automated by AI agents, make some IT leaders enthusiastic and give others pause. There are valid reasons for both feelings. And the stakes are even higher when you begin orchestrating multiple AI agents.

Every year, WSO2Con brings together developers, tech enthusiasts, and IT leaders from around the world to dive into the latest in APIs, identity management, integration, and now AI. It’s a space to learn from keynotes and hands-on sessions, share best practices, and get a first look at what’s new from WSO2. So when people walked into the AI Labs at WSO2Con, they expected to hear about AI agents. What they didn’t expect was to meet one, embodied inside a four-legged robot dog.