Building the Context Flywheel for AI Data Agents

Summary

In this episode Prukalpa Sankar, co-founder of Atlan, talks about what it takes to build a “context flywheel” for AI agents in data-intensive organizations. She explained why model intelligence alone isn’t enough to make AI useful in production, and how real performance depends on contextual intelligence: institutional knowledge, semantic meaning, procedural know-how, and access to the right tools. She also dug into how metadata catalogs are evolving into broader context layers that serve both humans and agents, and why agentic systems are changing the economics of metadata and governance work. Prakulpa shared Atlan’s perspective on bootstrapping context from existing systems such as warehouses, BI tools, query logs, and SaaS applications, then using simulation, traces, and human governance loops to improve agent accuracy over time.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data managementYour host is Tobias Macey and today I'm interviewing Prukalpa Sankar about strategies for building a context flywheel for your data agentsInterview

IntroductionHow did you get involved in the area of data management?You have spent several years working in the metadata catalog space with Atlan. What are the notable changes in scope, adoption, and application that you have seen since we last spoke (June 2022)?The recurring theme since the start of 2026 has been agentic augmentation of all engineering workflows, including data. How do you differentiate between data catalogs, semantic layers, agent memory, context layers, etc. when architecting an AI-powered data-oriented system?One of the perennial problems with data catalogs, business glossaries, master data management, etc. is the up-front investment required to get a real-world impact. How can agents help reduce the activation energy needed to get to that return on effort?One of the perennial problems in data engineering is fragmentation and siloing of data. This is exacerbated by AI systems due to the introduction of vector data as a new specialization. What are the forces that you are seeing play into the current set of tensions and the architectural primitives that we need to bring to bear to keep things maintainable?Since the introduction of transformer-based generative models we have been combating hallucinations. While we have made progress, it is still critical to ensure accuracy and trustworthiness when working with business data. What are the policy element