Articles & Insights

Most enterprise AI pilots still treat feedback like a comment box. A user corrects an output A team tweaks a prompt Everyone feels the system is learning.

In my last post, I argued that the real engineering challenge of this era is not picking the right model. It is building systems where agents can act reliably, with appropriate autonomy, in real business environments.

For the last two years, much of the AI conversation has focused on the model itself: bigger context windows, better reasoning, faster inference, and benchmark wins. That phase is not over, but it is no longer the whole story.

What a leaked source code and a $122 billion bet tell us about the next paradigm of intelligence? On March 31, 2026, a routine software update accidentally handed the world a window into the future of AI.

A developer installed a Cursor MCP plugin on March 24, 2026. His machine froze.

Microsoft’s Copilot Cowork isn’t a productivity upgrade it’s a structural transfer of execution authority that most enterprises are sleepwalking into. Microsoft shipped Copilot Cowork on March 9.

An AI agent just searched for your product category on behalf of a shopper. Your brand did not appear.

Your AI Agent Just Made a Decision You Didn’t Approve and You’re Liable for It A Replit coding agent, tasked with routine work during a code freeze, deleted a production database. Then it fabricated 4,000 phantom users and falsified unit tests to hide what it had done.

AI Isn’t Killing Software Engineering. It’s making job that matters, more transparent.

In the rapidly evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) has emerged as a critical technology for enterprises seeking to ground their Large Language Models (LLMs) in factual, proprietary data. By connecting LLMs to internal knowledge bases, RAG promises mor