This article walks through the design and implementation of an Agentic Retrieval‑Augmented Generation system using LlamaIndex, showing how to wrap multiple RAG engines as tools, orchestrate them with hierarchical AI agents, and scale the solution with tool retrieval for large document collections.
The article explains how zero‑intrusion eBPF provides full‑stack, high‑quality observability data that, when combined with large language models, enables AI‑driven agents to automate ticket handling, change impact analysis, and vulnerability triage, dramatically improving operational efficiency.
The article summarizes Andrew Ng’s presentation on AI agents, contrasting traditional single‑prompt large‑model usage with iterative agent‑based workflows, reporting experimental accuracy gains, and outlining four agent design patterns—reflection, tool use, planning, and multi‑agent collaboration—while discussing practical trade‑offs such as latency and token speed.
This article reviews the common business and technical architectures used in large language model (LLM) applications, explains AI Embedded, AI Copilot, and AI Agent modes—including single‑ and multi‑agent systems—and offers guidance on selecting appropriate technology stacks such as prompt‑only, function‑calling agents, RAG, and fine‑tuning.
This article surveys the emerging field of large‑language‑model (LLM) based agents, detailing their modular architecture—including profiling, memory, planning, and action components—while discussing critical challenges such as role‑playing, memory design, reasoning, multi‑agent collaboration, and outlining promising research directions and practical case studies.
This article explains how AutoDev 1.7.0 introduces a server‑side AI Agent that can be integrated into the IDE, offering three response types—Direct, TextChunk, and WebView—through configurable JSON endpoints, with practical demos, code snippets, and guidance for extending development workflows.
This article examines JD Retail's end‑to‑end large language model framework that combines supervised fine‑tuning, retrieval‑augmented generation, and ReAct‑based AI agents to overcome retail‑specific challenges, improve model accuracy, reduce hallucinations, and enable autonomous multi‑step business workflows.
This article walks through using large language model agents to create a theme‑park queue planning assistant for Baidu's Qianfan competition, covering the problem definition, dynamic‑programming‑style solution, prompt engineering, Python code generation, and step‑by‑step deployment in AppBuilder.
This article explores three key ways large language models and multimodal generative AI can enhance robotic process automation, from cognition‑boosted RPA and AI‑Agent collaboration to visual‑intelligent navigation, illustrating practical examples and future prospects for smarter digital workers.
This article explains the fundamentals of Robotic Process Automation (RPA), compares RPA with BPA and AI Agents, and provides a step‑by‑step tutorial for building and running an RPA robot using Robot Framework and the open‑source RPA Framework, including full code examples.
The article examines the rise of AI Agents as a bridge to AGI, analyzes their value, application domains, and user groups in enterprises, and proposes a layered architecture with model, data, ops, and agent components to guide practical implementation and integration.
This article explores how AI agents, particularly those built with LangChain, can enhance Text2SQL performance by decomposing queries, leveraging tools, memory, and planning, and provides practical code examples and future directions for developers.
This article explains the concept of AI Agents in the large‑language‑model era, details LangChain's implementation mechanics, shares practical challenges and optimizations encountered by NetEase Cloud Music, and provides step‑by‑step code examples and performance insights for building robust AI Agents.
This article compares Prompt Engineering and Supervised Fine‑Tuning for large language models, explains their principles, showcases common prompt patterns such as Chain‑of‑Thought, ReAct and Self‑Ask, outlines fine‑tuning stages and trade‑offs, and provides practical guidance on selecting the most suitable approach for specific enterprise AI Agent scenarios.
This article outlines a practical enterprise architecture for integrating large language models into a sales assistant, detailing knowledge ingestion, vector embedding, task planning, tool usage, and iterative dialogue, while introducing AI Agent concepts and open‑source frameworks such as LangChain.
