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.
The article "Data Enablement for AI: A Practitioner's Guide to 6 Advanced Architectural Patterns" discusses six production architectures that transform enterprise lakehouses into governed AI platforms, moving beyond simple chunk-and-embed approaches. It provides a practical guide for implementing these advanced patterns in real-world settings.
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.
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...
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.
A comprehensive guide to designing, orchestrating, and deploying AI agents—covering use cases, model selection, tool design, guardrails, and multi-agent patterns.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
arXiv:2610.14818v1 Announce Type: new Abstract: Agentic AI moves generative AI applications toward systems that coordinate reasoning, enterprise context, model inference, and external actions. Integr...
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.
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
arXiv:2604. 00073v3 Announce Type: replace-cross Abstract: There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously.
The paper introduces an architectural mediation approach that uses the Model Context Protocol (MCP) to bridge large language model (LLM) agents with data spaces. By implementing the Eunomia Agent, the mediation layer translates data space capabilities into structured, schema-driven tools that LLM agents can discover and invoke while respecting governance constraints. A prototype demonstrates end‑to‑end interaction across catalog discovery, metadata retrieval, and data service invocation without altering existing data space components, showing that protocol‑based mediation enables interoperable, standards‑aligned integration of AI agents into governed data‑sharing ecosystems.
arXiv:2608. 05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation.