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.
The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
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.
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.
The paper presents a taxonomy of architecture options for foundation-model-based agents, covering functional capabilities, non‑functional qualities, and operational aspects of design‑time and run‑time phases. It also introduces a decision model to guide critical design and runtime choices, aiming to streamline and improve the development of such agents. By unifying these classifications, the authors seek to reduce fragmentation in the field and provide a structured framework for architects and developers.
arXiv:2505. 21550v2 Announce Type: replace-cross Abstract: Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments.
The article "From One Agent to a Team: Understanding Codex Subagents" offers a practical guide on how to create specialist agents and manage their collaboration using the Codex Command Line Interface. It explains the process of defining subagents and coordinating their tasks to build a cohesive team of AI agents. The guide is aimed at developers looking to extend Codex’s capabilities through modular, specialized agent workflows.
arXiv:2607. 07397v1 Announce Type: new Abstract: Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs.