Systems | Development | Analytics | API | Testing

How integration forms the connective tissue of human-AI collaboration and enables the next evolution of enterprise intelligence.

Getting APIs to work with AI has become one of the major themes in the API space recently. And that’s not surprising because APIs are at the core of an AI’s ability to reach out into the world, to get access to data and information, and to invoke commands and workflows to act. This was always what APIs were for, but in this article we will dive a little deeper what that evolution looks like, and what that means for API governance and management.

When we think of a typical API, part of a production-ready setup generally includes a cache. This cache allows for similar requests to be served without having to do the entire roundtrip. But when it comes to AI applications powered by large language models, traditional caching falls short. This is because queries to an AI endpoint may look different in terms of how things are worded or phrased but actually mean the same thing semantically.

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.

In the rapidly shifting landscape of identity security, consistency is the ultimate measure of excellence. Today, we are proud to announce that for the fourth consecutive report, WSO2 has been recognized as an Overall Leader in the January 2026 KuppingerCole Leadership Compass for CIAM Platforms. Earning a leadership spot for many years is a significant achievement, but this 2026 recognition is particularly special.

When we started WSO2 in 2005, there was no question what programming language was right for developing server-side enterprise infrastructure: Java. However, as we go past our 20th year and look ahead at the next 10 to 20 years, it’s clear that we need to reflect on the way forward.

In a high-interest-rate environment, the most expensive asset a bank can hold is a signed partner contract that isn’t generating transaction revenue. For many regional banks, the 4–6 month gap between “contract signed” and “first transaction” is driven by manual compliance reviews, fragmented security processes, and custom integration work that delays go-live. We call this the “Integration Tax.”

With CMS-0057-F enforcement approaching in 2027, payer leaders face a decision that goes beyond compliance: how execution will be owned, scaled, and defended across the enterprise.

The recently released OWASP, “A Practical Guide for Securely Using Third-Party MCP Servers,” highlights a fundamental challenge in modern AI deployments: how do we govern, secure, and audit systems that are inherently non-deterministic? Unlike traditional, static software, AI agents dynamically adapt their execution paths, tool selection, and decisions based on context and real-time resources, allowing the same agent to achieve identical goals through entirely different approaches.