Building Practical AI Agents

AI agents are becoming useful when they move beyond demos and start solving real workflow problems. A practical agent should understand the user’s intent, retrieve the right context, use tools when needed, and return an answer that is grounded, concise, and easy to act on.

In my recent work, I have been exploring how Retrieval-Augmented Generation, voice interfaces, and LLM workflows can come together to build systems that feel more natural and reliable. The goal is not just to make an assistant that responds, but one that can work with real documents, domain knowledge, and user constraints.

This article is a placeholder. I will update it with a deeper write-up soon.