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, ...

The Model Context Protocol (MCP) changes this equation. Think of it as the "USB-C for AI." It's an open standard that allows ...

The Model Context Protocol (MCP) is redefining how artificial intelligence (AI) systems interact with external tools and services. By addressing the inherent limitations of large language models (LLMs ...

Making inherently probabilistic and isolated large language models (LLMs) work in a context-aware, deterministic way to take real-world decisions and actions has proven to be a hard problem. As we ...

LLMs and AI tools have transformed nearly every industry, including marketing. We’ve become accustomed to AI’s ability to: But as these models evolve, their capabilities are entering a new phase with ...

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 ...

When your mcp client talks to a server—maybe a retail bot checking inventory levels—they usually do a "handshake" to agree on a secret key. If you use ML-KEM, that handshake stays safe even if a ...

AI innovation today is moving faster than ever before, with leaps and bounds being made in the field on what seems like a weekly basis. Further to innovation that is directly related to or produced by ...