A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin
The article outlines five principles that guide the successful deployment of enterprise agent systems, illustrated with a real-world example from a $100M+ company. It explains how these principles help ensure that such systems can be trusted, verified, and improved over time. The post serves as a practical guide for building reliable agent-based solutions in production environments.
By Sheila Teo
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
By Shuai Guo
A step-by-step guide to building a data agent and conversational interface that let business users to explore data in natural language without SQL The post I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How.
By Jiayan Yin
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
By Sara Nobrega
The article discusses how to transition AI agents from prototype to production by implementing responsible AI practices, security measures, and governance frameworks suitable for enterprise use. It outlines the necessary architectural layers that ensure these agents operate safely and comply with organizational policies. The focus is on building robust, secure, and governed AI systems that can be reliably deployed in business environments.
By Partha Sarkar