The Linux Foundation’s 2026 Technology Talent Report reveals that while 97% of organizations plan to deploy AI, 57% lack capabilities in AI safety, risk management, and AI operations, making AI Ops and AI security the most sought‑after talent areas, with upskilling now the preferred hiring strategy.
The article analyzes why traditional hand‑over models fail as the number of AI agents grows, proposes a shared‑semantic and result‑driven management approach inspired by Salesforce and Palantir, and outlines a three‑layer organizational model with dual ownership to keep a digital workforce sustainable.
The article analyzes Palantir's Foundry and AIP approach in a mortgage‑lending case, showing how unified business context, rule traceability, and ontology‑driven document and call processing turn AI from a standalone model into an end‑to‑end operational system.
The article explains how Palantir’s Foundry and AIP combine data integration, ontology‑based business context, and rule management to turn large‑model AI into executable intelligence, illustrated by Freedom Mortgage’s 90‑day rollout of compliance, document, and call‑handling applications that link rules, documents and customer interactions into a unified, actionable system.
The article details Qunar’s AI‑driven SQL Agent project, describing how data‑governance, multi‑agent architecture, prompt design, and RAG techniques were combined to reduce data‑access latency, raise query accuracy above 85%, and streamline the end‑to‑end data‑service workflow for business users.
The article explains why most AI projects fail, introduces the concept of the Harness era where engineering and tooling outweigh pure algorithms, presents the RIDE methodology for enterprise AI adoption, and shows how AI‑native organizations transform roles, processes, and culture to achieve sustainable competitive advantage.
Kuaishou’s R&D efficiency team built the KOncall intelligent on‑call platform, integrating LLM‑based retrieval‑augmented generation, Redis Pub/Sub streaming, OCR multimodal parsing, FAQ knowledge ops, and custom reranking, which raised automated query interception from 15% to 55% and processed over 116 000 requests, turning on‑call from a bottleneck into a capability starter.
This article explains why the Harness architecture—linking prompts, context, and runtime support—is the decisive factor that turns large language models from demo prototypes into reliable production agents, detailing its core capabilities, structural components, execution loop, design trade‑offs, and industry trends.
When a service’s P99 latency spikes to seconds and CPU hits 90% without logs, Arthas lets you inspect the JVM in real time without code changes, and its AI‑driven MCP extension automates command selection, enabling developers to diagnose, trace, decompile, and monitor issues through simple natural‑language prompts and Spring Boot integration.
The OpenClaw CMS plugin 0.1.2 upgrades observability for AI agents by fully restoring multi‑round execution traces, stabilizing concurrent chains, adding STEP spans, and quantifying agent metrics, turning raw trace graphs into actionable insights for debugging, testing, cost control, and cross‑team collaboration.
Enterprise AI teams often see impressive results with single‑turn prompts, but when tasks become long‑running and complex, models lose context, produce faulty code, and require heavy manual intervention; the Harness framework provides a full‑lifecycle control system that stabilizes agents, manages knowledge, and ensures reliable production deployment.
This guide walks you through the essential steps to transform a freshly installed OpenClaw instance into a stable, production‑ready AI assistant, covering troubleshooting, configuration files, memory persistence, model selection, security hardening, Telegram integration, browser setup, and automated heartbeat and cron management.
This article traces the evolution of large‑model capabilities from early plugins to the standardized Agent Skills framework, explains the core concepts, technical composition, and progressive disclosure mechanism, and provides a step‑by‑step practical guide for building, configuring, and deploying Skills across ecosystems.
This article details the design and deployment of a multi‑layer LLM system that automatically reads massive creator group chats, extracts structured insights, mitigates hallucinations with dual‑model verification, uses few‑shot prompting for stable output, and delivers real‑time risk alerts and operational reports.
This article explores Bilibili’s implementation of a large‑language‑model‑driven intelligent assistant that helps troubleshoot massive offline and real‑time data processing tasks, detailing the platform’s five‑layer architecture, common failure causes, and how AI can streamline issue resolution.
This article explains how Bilibili leverages a large‑language‑model‑based intelligent agent to diagnose and resolve failures and slowdowns in its massive big‑data platform, detailing the platform architecture, workload scale, common user issues, and the need for automated assistance.
The Zhiyu model, a 32‑billion‑parameter SRE‑focused LLM, combines extensive domain knowledge, enhanced professional skills, and deterministic RAG to deliver precise, actionable insights for intelligent operations, backed by a robust multi‑source training pipeline, staged training, and flexible deployment options.
This guide explains how to design and implement a complete observability solution for large‑model AI services on Alibaba Cloud, covering architecture, core metrics, logging standards, demo code, log collection, dashboard design, alerting, monitoring tools, troubleshooting SOPs, and recovery procedures.
In this interview, senior AI product operations manager Lu Xinting shares how to identify high‑value AI scenarios, apply three practical metrics, build a closed‑loop AIGC operation framework, and design user incentives to achieve product‑market fit for large language models in logistics.
The DA Data Intelligence Conference in Shenzhen showcased SF Tech’s breakthroughs in large‑model AI, revealing how their proprietary multimodal models, RAG innovations, and agent platforms dramatically improve logistics decision‑making, resource scheduling, and customer service across multiple industries.
This guide explains how to transform Elasticsearch from a simple log repository into an intelligent operations AI assistant that provides real‑time monitoring, natural‑language query, automated troubleshooting, security threat detection, and low‑code interaction, covering architecture, deployment steps, sample queries, visualization, and resource cleanup.
This article explains how semantic enrichment, vector retrieval, hybrid search, and clustering can be combined to evaluate large language model inputs and outputs, improve debugging, ensure compliance, and enhance user intent understanding in AI applications.
This article explains LLMOps—its goals, core activities, benefits, best practices, and how using an LLMOps platform like Dify can dramatically cut development time, simplify prompt engineering, data preparation, monitoring, and deployment of large language models.
This article provides a comprehensive, step‑by‑step guide for developing large‑language‑model (LLM) applications, covering prompt design techniques, n‑shot and chain‑of‑thought strategies, retrieval‑augmented generation, structured I/O, workflow optimization, evaluation pipelines, operational best practices, and team organization to create reliable, scalable AI products.
Opal is iQIYI's end‑to‑end machine‑learning platform that integrates feature production, sample construction, model training, and deployment with big‑data services, addressing duplicated effort, weak data processing, and fragmented pipelines to boost efficiency across recommendation, advertising, and risk‑control scenarios.
This article introduces LLMOps by defining large language model operations, explains the three stages of LLM development, details modern fine‑tuning methods such as PEFT, Adapter, Prefix, Prompt and LoRA, outlines the architecture for building LLM applications, discusses the main difficulties of agent‑based deployments, and presents practical solutions including Prompt IDE, low‑code deployment, monitoring and cost control.
This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.
This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.
This article outlines the background, standards, evaluation items, process, and registration details of a newly launched MLOps development management maturity assessment designed to accelerate AI model delivery and improve operational efficiency across teams.
DiDi’s shift to machine‑learning‑driven ride‑hailing services revealed major QA challenges—data and feature quality, model verification, and API stability—prompting a four‑pillar framework and a unified “Strategy‑Center 1.0” platform to systematically monitor, evaluate, and improve model effectiveness, bias paths, and feature discovery.
This article explores how AI-driven AIOps can turn massive operational log data into actionable insights, detailing the five‑level AI capability model, real‑world implementation scenarios, and industry case studies that demonstrate the value of an intelligent log center.
