The article discusses how modern architectures create precise, cost‑efficient context for enterprise AI agents, moving beyond Retrieval‑Augmented Generation (RAG). It presents six advanced architectural patterns that serve as a practical guide for practitioners. The focus is on engineering context layers that improve the performance and efficiency of AI systems in enterprise settings.
By Partha Sarkar
The article "Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns" discusses six production-oriented architectures designed to build autonomous, reliable, and scalable large language model (LLM) agents, moving beyond basic ReAct loops. It provides a practitioner-focused guide to implementing these advanced patterns in real-world systems.
By Partha Sarkar
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin
Beyond basic graph retrieval: six production-oriented architectures for combining semantic search, knowledge graphs, and LLM reasoning.
The post GraphRAG: A Practitioner's Guide to 6 Advanced Architec...
By Partha Sarkar
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.
By Thuwarakesh Murallie
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.
By Shafeeq Ur Rahaman
What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .
By Hugo Lu
Towards Data Science has released a video showcase titled "Introducing ShipAI," which highlights real‑world AI work. The post announces this new visual resource and its focus on practical AI applications. It is positioned as a first look into the platform’s capabilities.
By TDS Editors
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
The article "A Scaffold at the Gate: Building AI Applications That Can Evolve" reflects on the necessity for data scientists to adopt software engineering principles in the context of AI agents that generate code. It discusses how these principles can help create AI applications capable of evolving over time. The piece was originally published on Towards Data Science.
By Erika Gomes-Gonçalves
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
By Shuai Guo
Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through rigid, reactive proces...