Towards Data Science
Jul 18
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.
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Towards Data Science
Aug 18
Building Enterprise Agent Systems that People can Trust, Verify and Improve
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.
Towards Data Science
Aug 4
Using Agents as Tools
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
Towards Data Science
Aug 6
I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How.
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.
Towards Data Science
Jun 3
What AI Agents Should Never Do on Their Own
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 .
Towards Data Science
Aug 18
From Prototype to Production: The Architecture Behind Secure & Governed AI Agents
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.
Towards Data Science
Sep 3
How to Solve the Right Problem in the Age of Agentic AI
The article "How to Solve the Right Problem in the Age of Agentic AI" presents a practical framework aimed at reducing uncertainty before agents accelerate implementation. It offers guidance on identifying and addressing the most relevant problems in the context of increasingly autonomous AI systems.
Towards Data Science
Aug 27
How to Work with AI Coding Agents
The article "How to Work with AI Coding Agents" offers a practical guide aimed at improving code quality rather than merely increasing quantity. It focuses on strategies and best practices for effectively collaborating with AI coding tools to produce better code. The post was originally published on Towards Data Science.
Towards Data Science
4d ago
How to Design Architectural Guardrails Around AI Agents
The article "How to Design Architectural Guardrails Around AI Agents" discusses essential agent design patterns that data engineers should understand. It emphasizes the importance of establishing clear architectural boundaries to ensure AI agents operate safely and effectively within larger systems. The post was originally published on Towards Data Science.
Towards Data Science
Aug 26
Is Agentic AI Just Automation?
The article titled "Is Agentic AI Just Automation?" argues that many so‑called agents are merely flowcharts in disguise. It explains why this misconception exists and suggests what kinds of systems should be built instead to achieve true agentic AI.
Towards Data Science
1d ago
Where the Agent Development Lifecycle Fits
The article "Where the Agent Development Lifecycle Fits" discusses how to coordinate the development of agent capabilities with the applications they power. It highlights the importance of aligning agent creation processes with the needs of the end‑use cases they support. The piece appears on Towards Data Science and focuses on integrating agent development into broader application workflows.
Towards Data Science
Aug 22
Building a Proper Backend for My LangGraph AI Agent
The article discusses transforming a demo LangGraph AI agent into a fully functional backend capable of handling real booking data. It outlines the steps and considerations involved in building a robust system that supports live data processing and integration. The focus is on practical implementation details rather than theoretical concepts.