Systems | Development | Analytics | API | Testing

One API request took 302 milliseconds. Nothing failed. CPU was mostly idle. The response was correct. The trace made the problem obvious: eight inventory calls, each waiting for the previous one. But the trace could not tell me why the application made eight calls, or whether changing their execution would preserve the response. It showed the shape of the wait, not the input that created it.

There’s something satisfying about contract testing the contract-testing tool. The SmartBear MCP Server is the integration layer between AI coding assistants and the PactFlow API, so when we decided it needed Pact consumer tests of its own, we were subjecting our own code to the same standards that we recommend.

Healthcare is becoming more connected than ever, but most hospitals still rely on decades old systems to exchange clinical data. At the same time, AI, cloud platforms, patient apps, and new interoperability regulations are driving organizations toward modern API based architectures. According to Deloitte, 90% of healthcare executives expect digital technology adoption to accelerate in 2025, while McKinsey found that 85% of healthcare leaders are already exploring or implementing generative AI.

For decades, APIs have been the universal handshake of the digital economy. You want data? Call an endpoint. You want to trigger an action? POST a payload. APIs are simple, predictable, and everywhere. But in 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard that dramatically changed how systems speak to one another. MCP enables AI agents to connect with external apps without custom code.

Most test automation tools are sized for one kind of team. The ones a lean QA group can adopt inside a quarter tend to buckle when a compliance officer asks who approved a change and when. The ones large enterprises standardize on often need a dedicated platform team before anyone writes a single test. Anyone who has evaluated tooling knows the feeling of reading a glowing review and realizing it was written by someone at a company nothing like theirs.

The rapid adoption of edge computing is fundamentally changing how enterprises process and test data. According to Gartner, 75% of enterprise-generated data will be created and processed outside centralized clouds by 2025 – a dramatic rise from just 10% a few years ago. This surge is fueled by the proliferation of IoT devices and smart endpoints, which generate massive data volumes and strain traditional, centralized cloud models.

In software delivery circles, it’s still common to hear, “Performance testing can wait until just before release.” But that thinking is increasingly out of step with the realities of modern development. Continuous performance testing (CPT) is no longer a nice-to-have for high-maturity organizations – it’s becoming the baseline for any team serious about reliability and user experience. Treating CPT as optional isn’t a conservative move; it’s a risk.

Consider a SaaS provider experiencing a sudden surge in demand. After years of steady expansion, a viral integration sends active user sessions soaring – tripling overnight. Onboarding speeds up, clients invite their own users, and the platform expands into new regions. With this momentum, the risks escalate: any downtime or performance issue now threatens not only revenue but also customer trust and regulatory standing.

AI is writing more of your codebase every quarter. A new Sauce Labs report surveying 400 U.S. executives and engineering leaders shows most organizations still can't verify it's safe to ship, and the incidents are piling up.