Postcard from a vacation: I went to Vienna for the palaces. I came back thinking about LLMs, meaning, and what we're really building in Enterprise AI. A century-old philosophy debate turned out to have a lot to say about how we ground these systems. A few reflections from the trip.
The Difference That Makes a Difference: The case for Data in Enterprise AI
In 1909, biologist Jakob von Uexkull coined the term umwelt — the perspective-dependent universe of salient information that an organism uses to navigate the world. Every species constructs its own. The electromagnetic spectrum is vast, but humans perceive only a narrow band that we call 'light'. That is our umwelt: it defines our experience of... Continue Reading →
#82: How do we evaluate Agentic AI Systems? Part-2
Continuing from my previous post, this is an attempt to define metrics for measuring the quality of Agentic AI Systems
#81: How do we evaluate Agentic AI Systems? Part-1
I recently met with the Head of Data Science at one of the largest media conglomerates and as often happens these days, evaluation and quality of AI Agents came up. One of the most important problems he has been trying to solve is how to evaluate the quality of AI agents, and once in production,... Continue Reading →
#80: Measuring Human and AI Agents
Since 2023, we have been hearing doomsayers talking about the coming AI Apocalypse that will take our jobs, run economies etc. And they keep pointing to the impressive performance gains and the reasoning capabilities of the frontier models. Meanwhile, those of us in Enterprise AI continue to be frustrated by the temptation to treat these... Continue Reading →
Celebrating the learning and curious mindset 