Systems | Development | Analytics | API | Testing

Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait.

Most AI applications start the same way: wire up an LLM, stream tokens to the browser, ship. That works for simple request-response. It breaks when sessions outlast a connection, when users switch devices, or when an agent needs to hand off to a human. The cracks appear in the delivery layer, not the model. Every serious production team discovers this independently and builds their own workaround. Those workarounds don't hold once users start hitting them in production.

Organizations over the years have seen the writing on the wall: The future is cloud. Now, these companies and their DevOps teams areevolving, innovating, and pursuing new technologies, to gain a competitive edge and create new efficiencies. One of the ways they’re doing this is through application migration to cloud. In this blog, I’ll detail the nuances of application migration and how to best manage data during it, including various challenges and their solutions.

Government and defense agencies require extreme security measures to protect sensitive data like classified intelligence and military operations. Air-gapped systems, which are physically isolated from external networks, provide a robust solution by ensuring no remote access is possible. These systems are critical for deploying large language models (LLMs) safely in secure environments, enabling advanced AI capabilities like intelligence analysis and mission planning without risking data breaches.

The JavaScript debugger statement is a built-in keyword that tells the JavaScript engine to pause execution at a specific line of code. When execution stops, you can inspect variables, function scope, and the call stack using developer tools. It is commonly used during development to analyze how values change and where logic breaks, without relying on repeated logging or assumptions. No more guesswork. No more partial truths.

“API strategy” is a term prominently established in the ecosystem and heavily discussed, implemented, and followed by organizations. The term is more relevant now since API strategy has become, for the most part, AI strategy, since AI agents and services are now consuming APIs and tools to work towards business-specific goals under human tutelage. So the longstanding definition and scope of API strategy must take into account AI consumers.

The WSO2 API Platform offers an MCP Gateway that sits between MCP clients and the MCP servers they use, applying security, access control, rate limits, observation, and policy enforcement across all tool calls. Instead of requiring teams to write these controls directly within their MCP servers, the platform extends its existing API governance layer to cover MCP traffic.