Category: Artificial Intelligence - Página 2

Lately, there’s been talk of AI agents everywhere. Every company has their roadmap full of “agents that will revolutionize this and that,” but when you scratch a little, you realize few have actually managed to build something useful that works in production.

Recently I read a very interesting article by LangChain about how to build agents in a practical way, and it seems to me a very sensible approach I wanted to share with you. I’ve adapted it with my own reflections after having banged my head more than once trying to implement “intelligent” systems that weren’t really that intelligent.

Two protocols, two philosophies

In recent months, two protocols have emerged that will change how we build AI systems: Agent2Agent Protocol (A2A) from Google and Model Context Protocol (MCP) from Anthropic. But here’s the thing: they don’t compete with each other.

In fact, after analyzing both for weeks, I’ve realized that understanding the difference between A2A and MCP is crucial for anyone building AI systems beyond simple chatbots.

The key lies in one question: Are you connecting an AI with tools, or are you coordinating multiple intelligences?

In my years developing software, I’ve learned that the best tools are those that eliminate unnecessary friction. And LM Studio has just taken a huge step in that direction: it’s now completely free for enterprise use.

This may sound like “just another AI news item,” but for those of us who have been experimenting with local models for a while, this is an important paradigm shift.

The problem that existed before

Since its launch in May 2023, LM Studio was always free for personal use. But if you wanted to use it in your company, you had to contact them to obtain a commercial license. This created exactly the type of friction that kills team experimentation.

A few years ago, many AI researchers (even the most reputable) predicted that prompt engineering would be a temporary skill that would quickly disappear. They were completely wrong. Not only has it not disappeared, but it has evolved into something much more sophisticated: Context Engineering.

And no, it’s not just another buzzword. It’s a natural evolution that reflects the real complexity of working with LLMs in production applications.

From prompt engineering to context engineering

The problem with the term “prompt engineering” is that many people confuse it with blind prompting - simply writing a question in ChatGPT and expecting a result. That’s not engineering, that’s using a tool.

Yet another protocol promising to change everything

When IBM Research announced the Agent Communication Protocol (ACP) as part of the BeeAI project, my first reaction was the usual one: “Oh, just another universal protocol”. With nearly 30 years in this field, I’ve seen too many “definitive standards” that ended up forgotten.

But there’s something different about ACP that made me pay attention: it doesn’t promise to solve all the world’s problems. It simply focuses on one very specific thing: making AI agents from different frameworks talk to each other. And it does it in a way that really makes sense.

Confession of a converted skeptic

When Anthropic announced the Model Context Protocol (MCP) in November 2024, my first reaction was: “Ah, another protocol promising to solve all integration problems”. As a DevOps Manager who has seen dozens of “universal standards” born and die, I have reasons to be skeptical.

But after several months watching MCP be massively adopted - OpenAI integrated it in March 2025, Google DeepMind in April - I decided to investigate beyond the hype. And I have to admit something: I was wrong.

The future of shopping is here, and Walmart is leading a quiet revolution that will forever change how we interact with retail. While many companies are still experimenting with ChatGPT and basic generative AI tools, the Arkansas giant has taken a quantum leap toward Agentic AI, developing autonomous systems that not only recommend products but act, decide, and execute complete tasks on their own.

In this deep analysis, we’ll explore how Walmart is building a future where AI agents don’t just assist humans but operate as true autonomous collaborators, transforming from the shopping experience to the most complex internal operations.

Creating long, well-founded articles has traditionally been a complex task requiring advanced research and writing skills. Recently, researchers from Stanford presented STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking), a revolutionary system that automates the Wikipedia-style article writing process from scratch, and the results are truly impressive.

In this detailed analysis, we’ll explore how STORM is transforming the way we think about AI-assisted writing and why this approach could forever change the way we create informative content.