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.
Agent Harness is the full‑stack software layer that turns a stateless LLM into a capable, memory‑aware, tool‑using AI agent, and the article breaks down its three engineering layers, twelve production components, execution loop, and design trade‑offs across Anthropic, OpenAI, LangChain and other frameworks.
The article analyzes OceanBase’s newly released lake‑house AI database, focusing on its multimodal table feature that unifies storage, transaction, governance and hybrid search for images, PDFs, vectors and other non‑structured data, addressing the data‑consistency and latency challenges of Agent‑driven applications.
This article presents a detailed technical analysis of multimodal GraphRAG, covering document‑intelligence parsing pipelines, multimodal graph indexing, retrieval generation flows, the role of knowledge graphs in chunk association, comparative evaluations of RAG, GraphRAG and KG‑QA, and practical takeaways for building efficient RAG solutions.
The article analyzes Palantir's Agent Stack, showing that while building capable AI agents is now easy, enterprises hesitate to deploy them at scale because trust, state persistence, observability, and governance must be engineered through durable orchestration, ontology mapping, and continuous optimization.
Large language models may know a lot, but they still fail at concrete tasks because knowledge must be transformed into actionable, experience‑based skills; the article analyzes how self‑evolving AI agents require skill libraries, context‑aware representations, and continuous practice‑driven knowledge production rather than static documentation.
The article provides a comprehensive analysis of the Agent Harness concept—defining it as the full software infrastructure that enables large language models to act as autonomous agents, detailing its three engineering layers, twelve core components, execution loop, framework implementations, and key design decisions that affect production‑grade performance.
The article analyzes Apache Ossie's transition to an Apache incubated open semantic model, explaining how it aims to standardize business semantics across data platforms for AI agents, while highlighting its current focus on structural interoperability, governance, and the gaps that remain in conceptual and execution semantics.
The article analyzes how traditional data‑lake‑model‑cloud architectures expose three critical flaws for agentic AI—massive data movement, fragmented logging, and split compute—then details Tencent Cloud's Big Data AI DLC solution that unifies Spark and Ray on a single lake to enable in‑place processing, closed‑loop training, and cost‑effective iteration.
The Data for AI Beijing meetup revealed that Data Agents plateau at about 70% accuracy without a well‑defined semantic (context) layer, but can reach the 95% production threshold once that layer is built, highlighting a shift from engine‑centric to metadata‑centric architectures, six‑round convergence practices, and large‑scale metadata deployments.
At Palantir AIPCon 9, the U.S. Navy unveiled Ship OS, an AI‑native operating system that embeds autonomous agents into submarine construction, automating change‑order processing, providing real‑time supply‑chain visibility, and enabling dynamic production planning that reduces decision cycles from days to minutes.
