A trip into the mind of Wajdi

  This article is the write-up of my Talk at the Paris Machine Learning meetup The hardest feature in production RAG is not retrieval. It is judgment. A funny thing happens when you build your first search agent. At first, it feels magical. You take a messy pile of knowledge: PDFs, internal templates, business rules, operational instructions, store information, support documents. You chunk everything. You embed it. You retrieve the closest passages. You send them to an LLM. The model writes an answer. In a demo, everyone smiles. Then real users arrive. They ask half-questions. They use old names for new processes. They ask about rules that changed last week. They mix several requests in one sentence. They paste personal information. They ask things that look answerable, but are not actually supported by the documents. And the system does exactly what you built it to do. It answers. That was the uncomfortable lesson behind my Paris Machine Learning Meetup talk: A good search agent i...

The Complete Guide to Agentic AI in 2026: From Chatbots to AI Coworkers A plain-English introduction to what AI agents are, how they work, and why everyone is suddenly talking about them. A few years ago, the main question was simple: “What can ChatGPT answer?” Then the question became: “What can AI help me write, summarize, code, or analyze?” Now, in 2026, the question is shifting again: What can AI actually do for me? That shift is why everyone is talking about agentic AI . The term sounds technical, maybe even a little overhyped. But the basic idea is simple: A chatbot answers…. An AI agent acts. A chatbot waits for your prompt. An AI agent can take a goal, break it into steps, use tools, check what happened, and continue working toward the result. That does not mean AI agents are magic. It does not mean they are fully autonomous employees. And it definitely does not mean you should let them do anything without supervision. But it does mean we are moving from AI that only talks to A...