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 ...

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; ...

The framework provided by MCP allows agents to access and engage with databases, tools, apps and agents in real time in a united way.

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

Anthropic’s model context protocol (MCP), the ‘plug-and-play bridge for LLMs and AI agents’ to connect with external tools, has received a major update one year after its launch. The developer of ...

The past ten years have seen incredible advancements in the realm of Artificial Intelligence, but paradoxically, some of the most overt shortcomings of AI are still based not on intelligence but on ...

What if the way AI agents interact with tools and resources could be as seamless as browsing the web? Imagine a world where developers no longer wrestle with custom-built adapters or fragmented ...

When organizational context is embedded once and inherited everywhere, AI stops behaving like a stateless chatbot and starts ...

Imagine you’ve trained or fine‑tuned a chatbot or an LLM, and it can chat comfortably without any serious hiccups. You feed it a prompt and it responds. However, it’s stuck in a bubble: It only knows ...

An interface between an AI language model and external sources such as a database. The Model Context Protocol server (MCP server) determines what the model can access. The MCP client, typically an AI ...