The DoT Blog

  Logo courtesy of Databricks Over the last couple of years, the conversation around AI in enterprise software has moved quite quickly. Not long ago, the focus was on large language models (LLMs) and generative assistants; today, the discussion is shifting toward something more operational: agentic systems , AI agents capable of reasoning, planning, and acting across workflows. With the introduction of Genie Code , this past March, Databricks is making a clear statement about where it believes the future of data engineering and analytics is heading. The announcement positions Genie Code , the company’s tool designed for data teams for AI solution development, as a tool that allows developers and data professionals to build, orchestrate, and operate AI agents that interact directly with data environments. The interesting part is not just the tool itself. It’s, in my view, what it signals about the direction of the modern data platform.   From Data Pipelines to Agenti...

  Teradata logo courtesy of Teradata, Inc. For the last two years, the enterprise AI market has been stuck in an awkward in-between state. Plenty of pilots, plenty of proofs of concept, but far fewer systems that run the business. So, with its newly announced Enterprise AgentStack, Teradata is clearly trying to address this gap, not by introducing yet another model, but by focusing on how AI agents are built, governed, and deployed at enterprise scale. What makes this announcement interesting, in my view, isn’t the buzzword density; it’s the direction of travel.   From “AI features” to agentic systems AgentStack is positioned as a framework for building and running AI agents that operate directly on governed enterprise data. The emphasis is not on experimentation but on operational AI: agents that can reason, retrieve, and act within defined business constraints. This matters because most enterprise AI failures don’t happen at the model layer; they happen at the integration ...