Traditional Natural Language Interfaces (NLIs) to data often necessitate users to provide detailed, step-by-step instructions, reflecting an assumption of user familiarity with the underlying data and systems, which can limit accessibility. The emergence of Large Language Models (LLMs) has, however, revolutionized NLIs, enabling them to perform sophisticated reasoning, decision-making, and planning multi-step actions in diverse environments autonomously. In this talk, I will discuss how these language models as agents facilitate a paradigm shift towards moving beyond traditional code generation to more autonomous and user-friendly NLIs, capable of understanding high-level objectives without requiring intricate directives. I will also present our latest work in this direction, including instruction-finetuned retrievers for diverse environment adaptation, the enhancement of LLM capabilities with tool integration, and the development of open, state-of-the-art LLMs and platforms for constructing such language agents. The talk will conclude with an exploration of the current and future research prospects in this rapidly evolving domain.