Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
This article summarizes the Agent Harness Engineering survey, outlining the evolution from Prompt to Context to Harness engineering, presenting the seven‑layer ETCLOVG framework, benchmark findings, and the shift toward platform‑level observability, governance, and trace‑native evaluation for reliable AI agents.
This guide outlines a systematic, engineering‑focused approach to using OpenAI's Codex, covering context provision, prompt structuring, configuration management, skill creation, automation, and common pitfalls to help developers turn Codex into a reliable, continuously improving teammate.
This article breaks down the concept of an Agent Harness—a complete software infrastructure that surrounds large language models—covering its definition, three engineering layers, twelve core components, step‑by‑step execution flow, and the trade‑offs that determine production‑grade performance.
The survey "Agent Harness Engineering: A Survey" reveals how agent systems have evolved from prompt engineering to context engineering and now to harness engineering, introduces the seven‑layer ETCLOVG framework, shows benchmark gains from better harnesses, and argues that observability, governance, and trace‑native evaluation are essential for production‑grade AI agents.
Claude Opus 4.8, released just 43 days after 4.7 at the same price, tops the GDPval‑AA leaderboard with 1890 Elo, beats GPT‑5.5 by 121 points, cuts steps by 15% and tokens by 35%, achieves a perfect 0% lie and lazy rate, dominates SWE‑Bench, ProgramBench and FrontierSWE, and introduces massive parallel agent workflows that can rewrite 750 k lines of production code in 11 days, while Anthropic prepares the upcoming Claude Mythos and celebrates a $965 b valuation.
This article summarizes the 71‑page survey "Agent Harness Engineering: A Survey", detailing the shift from prompt to context to harness engineering, introducing the seven‑layer ETCLOVG framework, benchmark results showing up to 10× gains, and arguing that future competition will focus on the engineering shell surrounding LLM agents rather than model size alone.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based ad‑hoc analysis to a Lambda‑style architecture and finally to a lakehouse with generic incremental compute, cutting architecture complexity, resource and development costs by one‑third while delivering second‑level queries over trillions of rows.
DeepSeek researcher Chen Deli reports that using his DeliAutoResearch skill and a suite of AI agents, a 46‑page research paper was produced in six days with only two hours of human CPU time, revealing that the true limits of autonomous research lie in continuous knowledge accumulation and reliable self‑evaluation rather than model capability.
The article analyzes how Knora 4.0 integrates enterprise ontologies with large‑model AI to address six core challenges—hallucinations, unstable outputs, weak planning, poor responsiveness, data silos, and long cold‑start cycles—by detailing its layered architecture, autonomous agent Knora Claw, real‑world LED‑line case studies, and a three‑year roadmap toward fully autonomous enterprise systems.
The round‑table examines how Data Agent is moving from proof‑of‑concept to production, outlines its three‑stage evolution from NL2SQL to a general AI‑driven agent, highlights verification and semantic‑gap challenges, and presents expert views that the scaling tipping point could arrive within the next six to twelve months.
