Systems | Development | Analytics | API | Testing

API request logging is essential for secure and efficient APIs. It helps track performance, detect security threats, and meet compliance standards. Here's a quick breakdown of what you need to know: Why It Matters: Logging provides real-time insights, audit trails for investigations, and supports compliance.

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). It’s becoming a foundational layer in many AI-native workflows, especially when working with real-time or continuously updating data sources. We're excited to announce that Choreo now supports the deployment of MCP servers, empowering developers to integrate AI capabilities more efficiently into their applications.

The generative AI hype train doesn’t appear to be slowing down, with organizational adoption rising from 33% in 2023 to 78% by the end of 2024. In fact, bigger companies are leading the way in GenAI adoption, with the global AI market projected to grow annually by 36.6% between 2024 and 2030. However, GenAI growth isn’t following a linear path. Organizations are utilizing different AI approaches, depending on their specific use cases.

Look around yourself. We are swarming in the world of data and AI. From students at school using ChatGPT to complete their assignments to professionals using AI for market research, content creation, or even debugging code, everyone is leveraging the power of large language models (LLMs). Mr. Smith isn’t Googling his tax questions anymore; he’s asking an AI assistant.

For the past several years, there have been murmurs of an incoming convergence between API management and event management.

Kong has supported Redis since its early versions. In fact, the integration between Kong Gateway and Redis is a powerful combination to enhance API management. We can summarize the integration points and use cases of Kong and Redis into three main groups.

Your organization may be one of the many that is rushing to implement AI. But do you know where you fall on the AI maturity model? More than just a framework for understanding AI, the AI maturity model is a strategic guide that helps turn AI investments into tangible business results. A 2024 IDC study commissioned by Microsoft titled “The Business Opportunity of AI” found that organizations gain a $3.7x return for every $1 spent on generative AI.

AI code generators are revolutionizing the way software is developed. By leveraging the power of machine learning, these tools automate repetitive tasks, minimize human error, and accelerate the coding process. From GitHub Copilot to Keploy VS Code Extension, AI code generators are becoming essential for modern developers – whether they’re writing new features, debugging, or generating tests.

A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

Not long ago, I wrote about a growing problem in enterprise AI: agents that don’t talk to each other. You’ve got a customer relationship management (CRM) agent doing its thing, a data warehouse agent crunching numbers, a knowledge bot quietly surfacing documents—but none of them are sharing what they know. Instead of a smart, connected ecosystem, we’re stuck with isolated pockets of intelligence: an island of agents.