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 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 ...

The Model Context Protocol (MCP) for agentic AI has gained much traction since being introduced by Anthropic last November, and now it has a C# SDK. The MCP is a standard for integrating large ...

At this year's Build conference, Microsoft unveiled a major expansion of its agent-based AI platform, highlighting new tools to securely build, customize and orchestrate intelligent agents across ...

Microsoft Corp. believes we’re headed toward a future where artificial intelligence-powered agents will become pervasive in enterprise computing environments, so today it’s making it easier for those ...

The Model Context Protocol (MCP) is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. The architecture is straightforward: ...

We have all heard about model context protocol (MCP) in the context of artificial intelligence. In this article, we will dive into what MCP is and why it is becoming more important by the day. When ...

How quickly the world changes. In November 2022, OpenAI unleashed its generative AI chatbot, ChatGPT, on the world. Exactly two years later, Anthropic introduced the Model Context Protocol (MCP), its ...

On November 25, 2024, large language model (LLM) provider Anthropic open-sourced its Model Context Protocol (MCP). MCP provides a standardized way to connect an AI model, like the Claude family of ...

Released late last year by AI firm Anthropic, model context protocol (MCP) is an open standard designed to standardize the way AI systems, particularly large language models (LLMs), integrate and ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

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 ...