One of the biggest issues with large language models (LLMs) is working with your own data. They may have been trained on terabytes of text from across the internet, but that only provides them with a ...

As organizations push AI systems into production, IT teams are asking how to make models more dependable, secure and useful in real-world workflows. One approach gaining traction is the Model Context ...

AI agents and agentic workflows are the current buzzwords among developers and technical decision makers. While they certainly deserve the community's and ecosystem's attention, there is less emphasis ...

What if the next generation of AI systems could not only understand context but also act on it in real time? Imagine a world where large language models (LLMs) seamlessly interact with external tools, ...

Chances are, unless you're already deep into AI programming, you've never heard of Model Context Protocol (MCP). But, trust me, you will. MCP is rapidly emerging as a foundational standard for the ...

The Model Context Protocol (MCP) is an open source framework that aims to provide a standard way for AI systems, like large language models (LLMs), to interact with other tools, computing services, ...

Imagine a world where your AI tools don’t just work for you but work with each other—seamlessly, intelligently, and without the frustration of endless custom integrations. This isn’t a distant dream; ...

Ashay Satav is a Product leader at eBay, specializing in products in AI, APIs and platform space across Fintech, SaaS and e-commerce. Model context protocol (MCP) has been the talk of the town lately, ...

Spring AI 2.0 advances the Java framework for generative AI apps with a Spring Boot 4 baseline, cleaner agentic tooling, Model Context Protocol support and vendor-backed integrations including Azure ...