BestHub
Sign in
Tagged articles

AI systems

12 articles · Page 1 of 1
Data Party THU
Data Party THU
Jul 21, 2026 · Artificial Intelligence

Task Decomposition with Multi‑Agent Systems: Boosting Complex AI Workflows

This article reviews a Berkeley PhD thesis that argues powerful foundation models still need task decomposition, detailing six contributions—including LLM‑grounded diffusion, video diffusion, self‑correcting loops, detailed local description, adaptive parallel reasoning, and ThreadWeaver—to organize computation across multiple agents for more controllable, reliable AI systems.

AI systemsLLMmulti-agent systems
0 likes · 16 min read
Task Decomposition with Multi‑Agent Systems: Boosting Complex AI Workflows
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 30, 2026 · Artificial Intelligence

From Prompt to Loop: The Evolution of AI Development Paradigms

AI applications are shifting from single‑turn Q&A to systematic intelligence through four nested engineering stages—Prompt, Context, Harness, and Loop—each addressing communication, information supply, execution safety, and autonomous closed‑loop control, while exposing distinct limitations that drive the next paradigm.

AI systemsAgent ArchitectureContext Engineering
0 likes · 16 min read
From Prompt to Loop: The Evolution of AI Development Paradigms
Architect
Architect
Jun 19, 2026 · Artificial Intelligence

From Harness to Environment: The Next Engineering Layer for LLM Agents

The article argues that while Harness engineering still controls how agents run, the emerging focus on Environment engineering determines whether agents receive reliable, verifiable feedback, shaping their long‑term learning and safety in real‑world tasks.

AI systemsAgent EngineeringEnvironment Engineering
0 likes · 21 min read
From Harness to Environment: The Next Engineering Layer for LLM Agents
DataFunTalk
DataFunTalk
May 28, 2026 · Artificial Intelligence

The Most Comprehensive Survey on Agent Harness Engineering Revealed

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.

AI systemsAgentHarness Engineering
0 likes · 15 min read
The Most Comprehensive Survey on Agent Harness Engineering Revealed
ZhiKe AI
ZhiKe AI
Apr 25, 2026 · Industry Insights

Harness Engineering: The Hottest New AI Engineering Paradigm of 2026

Harness Engineering, now buzzing across the tech community, promises a ten‑fold productivity boost by replacing hand‑written code with a structured AI‑driven system, and the article breaks down its definition, evolution from Prompt to Context to Harness, core components, real‑world examples, and the associated risks and debates.

AI safetyAI systemsAutomation
0 likes · 9 min read
Harness Engineering: The Hottest New AI Engineering Paradigm of 2026
Sohu Tech Products
Sohu Tech Products
Apr 15, 2026 · Artificial Intelligence

Why Harness Engineering Is the Next Evolution in AI System Design

This tutorial explains the three-stage evolution from Prompt Engineering to Context Engineering and finally Harness Engineering, detailing their motivations, core components, practical implementations, and why stable, end‑to‑end AI agents require a full harness to manage tasks, context, tools, execution, state, and error recovery.

AI systemsAgent designContext Engineering
0 likes · 31 min read
Why Harness Engineering Is the Next Evolution in AI System Design
DeepHub IMBA
DeepHub IMBA
Apr 3, 2026 · Artificial Intelligence

Multi‑Aspect Embedding: Integrating Context Signals into Vector Similarity Search

The article analyzes how traditional vector database pipelines use external filters for context constraints and proposes the Aspect Database’s multi‑aspect embedding approach, which encodes contextual attributes directly into similarity vectors to enable unified, context‑aware retrieval for AI systems.

AI systemsANN searchEmbedding
0 likes · 9 min read
Multi‑Aspect Embedding: Integrating Context Signals into Vector Similarity Search
Woodpecker Software Testing
Woodpecker Software Testing
Mar 15, 2026 · Artificial Intelligence

How to Test AI That Sees, Listens, and Beyond: In‑Depth Multimodal Testing Cases

The article examines three real‑world multimodal AI testing scenarios—medical report generation, automotive V2X interaction, and e‑commerce AIGC content—detailing specialized assertion techniques, temporal‑sensitive chaos testing, and modality‑contract validation that dramatically reduce false positives, uncover hidden deadlocks, and boost content compliance.

AI systemsModality Contract TestingTSMCT
0 likes · 7 min read
How to Test AI That Sees, Listens, and Beyond: In‑Depth Multimodal Testing Cases
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jun 4, 2025 · Artificial Intelligence

What Is an AI Engineer? Roles, Skills, and the Future of LLM‑Powered Systems

This article examines the evolving role of the AI engineer, contrasting it with AI researchers, ML engineers, and software engineers, outlines essential skills such as prompt engineering, MLOps, and data integration, and predicts how AI engineering will become a pivotal, high‑demand discipline in the coming years.

AI EngineeringAI systemsAgentic RAG
0 likes · 17 min read
What Is an AI Engineer? Roles, Skills, and the Future of LLM‑Powered Systems
Architect
Architect
Jul 2, 2024 · Artificial Intelligence

Mooncake: A Separated Architecture for Large‑Language‑Model Inference

The article presents Mooncake, a split‑architecture inference platform for the Kimi LLM assistant, detailing its three elastic resource pools, the rationale for using Time‑Between‑Tokens over TPOT, and design choices for Prefill, KVCache, and Decode stages to improve latency and throughput.

AI systemsDistributed ArchitectureKVCache
0 likes · 9 min read
Mooncake: A Separated Architecture for Large‑Language‑Model Inference
DataFunTalk
DataFunTalk
Nov 14, 2019 · Artificial Intelligence

Building the Most Reliable Autonomous Driving Infrastructure at Pony.ai

This article outlines Pony.ai's comprehensive autonomous driving infrastructure, describing traditional internet back‑end components, additional vehicle‑mounted systems, large‑scale simulation, data challenges, and the reliability, performance, and flexibility practices needed to support rapid growth and safe robotaxi operations.

AI systemsInfrastructurePony.ai
0 likes · 15 min read
Building the Most Reliable Autonomous Driving Infrastructure at Pony.ai