Get the latest tech news
The new paradigm: Architecting the data stack for AI agents
Discover how to build a robust data stack for your AI agents and unlock the full potential of generative AI.
Fast-forward to 2024: there’s a flourishing ecosystem of language models, which both nimble startups and large enterprises are leveraging in conjunction with approaches like retrieval augmented generation (RAG) for internal copilots and knowledge search systems. The transition to AI agents marks a major shift from the automation we know and can easily give enterprises an army of ready-to-deploy virtual coworkers that could handle tasks – be it booking a ticket or moving data from one database to another – and save a significant amount of time. Teams don’t have to set up their data stack from scratch but adapt it with a focus on certain key elements to make sure that the agents they develop understand the nuances of their business industry, the intricate relationships within their datasets and the specific semantic language of their operations.
Or read this on Venture Beat
