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Agent Systems

32 articles · Page 1 of 1
Ray's Galactic Tech
Ray's Galactic Tech
Oct 21, 2026 · Backend Development

Agent Observability: From Traces to Task Failure Attribution — Production Design with Go, Java, OpenTelemetry & Kubernetes

This article presents a production-grade observability design for agent systems, covering task modeling, trace propagation, lease/fencing/checkpoint mechanisms, OpenTelemetry instrumentation in Go and Java, MCP integration, Kubernetes collector setup, PromQL dashboards, Argo CD release correlation, and fault injection experiments for failure attribution.

Agent SystemsFailure AttributionGo
0 likes · 51 min read
Agent Observability: From Traces to Task Failure Attribution — Production Design with Go, Java, OpenTelemetry & Kubernetes
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Oct 14, 2026 · Artificial Intelligence

Novix Achieves Scaled Co-Evolutionary RSI in Scientific Research

Novix, a platform by a Chinese team, implements co-evolutionary recursive self-improvement (RSI) where AI agents collaborate with top experts to solve frontier scientific tasks like model post-training, dam scheduling, and material prediction, creating a self-improving flywheel driven by human judgment.

AI for scienceAgent SystemsCo-Evolutionary RSI
0 likes · 18 min read
Novix Achieves Scaled Co-Evolutionary RSI in Scientific Research
Java Tech Enthusiast
Java Tech Enthusiast
Oct 12, 2026 · Artificial Intelligence

Harness Engineering: The Third Layer of AI Engineering for Production Systems

This article explains the progression from Prompt Engineering to Context Engineering to Harness Engineering, using analogies and concrete examples to show how each layer addresses a different challenge: task clarity, information access, and controllable execution in production workflows, with practical frameworks for each stage.

AI engineeringAgent SystemsContext Engineering
0 likes · 24 min read
Harness Engineering: The Third Layer of AI Engineering for Production Systems
Data Bricklaying Diary
Data Bricklaying Diary
Oct 7, 2026 · Backend Development

Agent Error Recovery: Why Retries Alone Fail — Idempotency & Reconciliation

This article explains why automatic retries after agent timeouts can duplicate business actions, and details how to distinguish call failures from executed side effects, implement execution-side idempotency guarantees, reconcile using original action identifiers, and define safe recovery rules with human escalation when outcomes remain unknown.

Agent SystemsError HandlingIdempotency
0 likes · 17 min read
Agent Error Recovery: Why Retries Alone Fail — Idempotency & Reconciliation
Chen Xiaogong's Room
Chen Xiaogong's Room
Sep 28, 2026 · Industry Insights

Cement Industrial Internet Platform Architecture: 56 Key Questions from Device Connectivity to Industrial Intelligence

This article presents a comprehensive architectural framework for cement industrial internet platforms, addressing 56 critical questions across connectivity, data semantics, AI/MLOps, digital twins, Agent systems, and cross-factory replication, grounded in AII and IIRA reference architectures.

Agent SystemsCross-Factory ReplicationMLOps
0 likes · 53 min read
Cement Industrial Internet Platform Architecture: 56 Key Questions from Device Connectivity to Industrial Intelligence
AI Large Model Application Practice
AI Large Model Application Practice
Sep 28, 2026 · Artificial Intelligence

JEV in Agent Systems: 8 High-Speed Decision Patterns for LLM Agents

This article details eight practical scenarios where JEV (Judgment and Evaluation) models accelerate agent decision-making, including ReAct loop control, web automation, tool routing, safety guards, task evaluation, model routing, RAG filtering, and real-time robotics control, showing how lightweight classifiers reduce latency and token costs.

Agent SystemsJevLLM agents
0 likes · 20 min read
JEV in Agent Systems: 8 High-Speed Decision Patterns for LLM Agents
Architects Research Society
Architects Research Society
Sep 1, 2026 · Operations

When Agents Vanish, Enterprise Work Must Persist: RHEOVELA’s Durable Process Model

RHEOVELA addresses the structural conflict that temporary AI agents cannot sustain long‑running enterprise tasks by persisting process instances through event sourcing, atomic idempotent command pipelines, and unified work items, ensuring reliable recovery, auditability, and continuity despite crashes, upgrades, or network failures.

Agent SystemsDurable WorkflowIdempotent Commands
0 likes · 5 min read
When Agents Vanish, Enterprise Work Must Persist: RHEOVELA’s Durable Process Model
Architect
Architect
Aug 8, 2026 · Databases

Ontology as the Semantic Control Plane for Agent Fact Systems

The article explains how an ontology—defining objects, relationships, constraints, and inferable boundaries within a domain—serves as a semantic control plane between the fact and action layers of an agent‑driven system, ensuring consistent interpretation, validation, and lifecycle management of business facts.

Agent SystemsData GovernanceGraph Databases
0 likes · 18 min read
Ontology as the Semantic Control Plane for Agent Fact Systems
Machine Heart
Machine Heart
Jul 25, 2026 · Artificial Intelligence

Is Human R&D Over? XYZ’s Top Search Agents Sweep Seven Benchmarks and Close the AI‑4‑AI Loop

XYZ AI Lab unveiled two Deep Search agents—35B‑parameter Aquila‑mini and 397B‑parameter Aquila‑pro—that set SOTA scores on seven public benchmarks, while demonstrating an AI‑4‑AI paradigm where hundreds of agents collaboratively drive a full‑stack research and improvement loop under human‑defined goals and verification.

AI4AIAgent SystemsBenchmarking
0 likes · 14 min read
Is Human R&D Over? XYZ’s Top Search Agents Sweep Seven Benchmarks and Close the AI‑4‑AI Loop
Data Bricklaying Diary
Data Bricklaying Diary
Jul 13, 2026 · Big Data

Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms

This article presents a comprehensive framework for ontology-driven data governance that extends beyond semantic modeling into a continuous operating system covering three governance object categories, a six-step scenario-based process, eight cross-functional roles, seven operating mechanisms, and integration with existing data governance capabilities to support AI-ready semantic assets and Agent actionability.

AI data engineeringAgent SystemsOntology-Driven Data Governance
0 likes · 16 min read
Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 9, 2026 · Artificial Intelligence

Why AI Self‑Improvement Must Begin with Harness Engineering

The article argues that true AI self‑improvement starts not with changing model weights but by engineering a robust outer system—called Harness—that orchestrates tasks, manages context, persists state, and enables agents to reliably modify and evaluate their own execution environment.

AI self‑improvementAgent SystemsHarness engineering
0 likes · 13 min read
Why AI Self‑Improvement Must Begin with Harness Engineering
vivo Internet Technology
vivo Internet Technology
Jun 24, 2026 · Artificial Intelligence

Defining the Right Way to Use AI: From Brain‑Like Models to Body‑Ready Agents

Although large‑language models now function like a brain, current AI agents suffer from an underdeveloped “body” – immature perception, action, and autonomic systems – and the field lacks converged best practices; tools like Harness act as an ICU, and real‑world cases such as AI‑generated PPT illustrate the urgent need to define proper usage patterns.

AI infrastructureAgent SystemsBest Practices
0 likes · 18 min read
Defining the Right Way to Use AI: From Brain‑Like Models to Body‑Ready Agents
Frontend AI Walk
Frontend AI Walk
Jun 16, 2026 · Artificial Intelligence

Why Better Feedback Loops, Not Smarter Brains, Define AI’s Upper Limits

Loop Engineering argues that the true performance ceiling of AI models stems from the quality of their feedback loops rather than raw intelligence, illustrating this through examples from bug‑fixing with GPT‑4, AlphaGo’s self‑play, and emerging agent frameworks, while also exposing practical pitfalls.

AI feedback loopsAgent SystemsAlphaGo
0 likes · 19 min read
Why Better Feedback Loops, Not Smarter Brains, Define AI’s Upper Limits
ThinkingAgent
ThinkingAgent
Jun 12, 2026 · Artificial Intelligence

From Hand‑Crafted Features to Harnesses: Five AI Engineering Leaps

Over the past four decades AI has undergone five fundamental shifts—from manual feature engineering, through deep neural networks and prompt engineering, to autonomous agents and finally the Harness era—each redefining core technology, scarce talent, and value creation, with the 2026 Harness era emphasizing system‑level scalability over model size.

AI engineeringAgent SystemsHarness²
0 likes · 16 min read
From Hand‑Crafted Features to Harnesses: Five AI Engineering Leaps
AI Engineer Programming
AI Engineer Programming
Apr 24, 2026 · Artificial Intelligence

From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design

The article traces the shift from Prompt Engineering to Context Engineering and now Harness Engineering, analyzing their origins, methods, limitations, and future directions such as Coordination, Intent, Ecosystem, and Cognition engineering, while emphasizing the decreasing human involvement and increasing system autonomy.

AI AgentsAgent SystemsContext Engineering
0 likes · 24 min read
From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 22, 2026 · Artificial Intelligence

Hands‑On Kimi K2.6 + Hermes: A Karpathy‑Style Step‑by‑Step Guide

This article presents a detailed, hands‑on tutorial for deploying Kimi K2.6 with Hermes and Obsidian, showcases multi‑modal video note‑taking, skill creation, self‑evolving LLM‑driven knowledge bases, large‑scale agent clusters, and discusses both the strengths and current limitations of the system.

Agent SystemsHermesKimi K2.6
0 likes · 10 min read
Hands‑On Kimi K2.6 + Hermes: A Karpathy‑Style Step‑by‑Step Guide
Architecture Musings
Architecture Musings
Apr 20, 2026 · Industry Insights

ThoughtWorks Technology Radar Vol. 34: Engineering Practices and Cognitive Shifts in the Agent Era

The article analyses ThoughtWorks' 34th Technology Radar, revealing how the rise of AI agents forces a reassessment of software engineering fundamentals, introduces semantic diffusion, cognitive debt, security risks, and highlights both newly adopted tools like Kafbat UI and Typer and a set of cautionary practices that demand tighter control and context engineering.

AIAgent SystemsSoftware Engineering
0 likes · 34 min read
ThoughtWorks Technology Radar Vol. 34: Engineering Practices and Cognitive Shifts in the Agent Era
DeepNoMind
DeepNoMind
Apr 19, 2026 · Industry Insights

How to Thrive in the AI Era: 5 Must‑Have Skills for 2026

The article outlines five core AI competencies needed in 2026—workflow automation, agent‑based systems, AI safety, self‑augmentation, and AI system evaluation—explaining why they matter, how to acquire them with tools like Zapier, n8n, Claude Cowork and custom GPTs, and how they keep professionals competitive.

AIAI safetyAgent Systems
0 likes · 10 min read
How to Thrive in the AI Era: 5 Must‑Have Skills for 2026
Machine Heart
Machine Heart
Apr 19, 2026 · Artificial Intelligence

Are Small Models the Core Component of Agent Systems?

The article analyzes how advancing small‑model capabilities are shifting agent system design from merely checking if a model can run under resource limits to evaluating its suitability for specific tasks, thereby redefining model selection logic and workflow partitioning.

Agent SystemsLLM scalingmodel selection
0 likes · 7 min read
Are Small Models the Core Component of Agent Systems?
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 15, 2026 · Industry Insights

Can Harness Engineering Turn AI Agents into Stable Software Systems?

The article analyzes how AI‑driven agents reshape software engineering, tracing historical precedents, exposing the uncontrollability of open‑loop AI code generation, and proposing Harness Engineering—a structured, feedback‑rich environment that turns continuous code generation loops into stable, controllable systems.

AIAgent SystemsHarness engineering
0 likes · 12 min read
Can Harness Engineering Turn AI Agents into Stable Software Systems?
ArcThink
ArcThink
Apr 6, 2026 · Artificial Intelligence

How Harness Engineering Let a 3‑Person Team Write 1 Million Lines of Code in 5 Months

Harness Engineering combines systematic prompts, context management, and robust validation loops to turn powerful LLMs into reliable agents, enabling a three‑engineer team to produce about one million lines of production code in five months and boosting LangChain’s benchmark ranking by 25 places, proving that well‑designed harnesses outweigh model improvements by an order of magnitude.

AI engineeringAgent SystemsContext Engineering
0 likes · 25 min read
How Harness Engineering Let a 3‑Person Team Write 1 Million Lines of Code in 5 Months
Advanced AI Application Practice
Advanced AI Application Practice
Apr 3, 2026 · Industry Insights

In-Depth Breakdown of the AI Business Architect Role and Interview Strategies

This article dissects the AI Business Architect position, detailing its true responsibilities, core competency formula, key role personas, supply‑demand matching scenarios, end‑to‑end technical architecture (including RAG and multi‑agent design), evaluation metrics, and provides concrete interview questions with model answers to help candidates prepare effectively.

AI architectureAgent SystemsInterview Prep
0 likes · 18 min read
In-Depth Breakdown of the AI Business Architect Role and Interview Strategies
ArcThink
ArcThink
Apr 1, 2026 · Artificial Intelligence

Inside Claude Code: 1,900‑File Source Dive Reveals Six‑Layer Architecture

After a source‑map leak exposed Claude Code’s 1,900 TypeScript files, this analysis dissects its six‑layer architecture, dynamic prompt assembly, four‑level caching, 60+ tool governance pipeline, six built‑in agents, five context‑compression strategies, and the real engineering trade‑offs hidden beneath the product.

AI engineeringAgent SystemsTool governance
0 likes · 31 min read
Inside Claude Code: 1,900‑File Source Dive Reveals Six‑Layer Architecture
Design Hub
Design Hub
Mar 28, 2026 · Artificial Intelligence

Why Harness Engineering Is Emerging as a New Kind of Company

The AI community is shifting its focus from model performance to building runnable, observable, and scalable agent systems, a trend illustrated by the rise of Harness Engineering, Open Agents Company, and Agent Matrix across X discussions, GitHub projects, and developer meetups.

AI AgentsAI infrastructureAgent Matrix
0 likes · 14 min read
Why Harness Engineering Is Emerging as a New Kind of Company
phodal
phodal
Mar 10, 2026 · Industry Insights

Why AI Needs a New Engineering Paradigm: From Human‑First to Agent‑Aware Systems

The article analyzes how generative AI is reshaping software development, exposing the mismatch between AI's rapid code generation and traditional human‑centric engineering processes, and proposes a new "Harness Engineering" model that emphasizes system readability, defensive boundaries, and continuous feedback loops for safe AI‑assisted coding.

Agent SystemsDevOpsengineering management
0 likes · 13 min read
Why AI Needs a New Engineering Paradigm: From Human‑First to Agent‑Aware Systems
Tencent Technical Engineering
Tencent Technical Engineering
Feb 2, 2026 · Artificial Intelligence

Why Neural Networks Are the Hidden Engine Behind Modern AI: From Basics to Large Language Models

This comprehensive guide walks through the fundamentals of neural networks, activation functions, training methods, and how they power large language models, while also covering tokenization, self‑attention, transformer architectures, AI infrastructure, and practical usage through agents and retrieval‑augmented generation.

Agent SystemsGPU infrastructureLarge Language Models
0 likes · 75 min read
Why Neural Networks Are the Hidden Engine Behind Modern AI: From Basics to Large Language Models
Tencent Technical Engineering
Tencent Technical Engineering
Jan 23, 2026 · Artificial Intelligence

Unlocking AI Infra: Distributed Inference, PD Separation, TileLang, and Next‑Gen Agent Infrastructure

This article surveys the 2025 AI infrastructure landscape, covering distributed inference with PD‑separation, dynamic DOPD scheduling, AFD attention‑FFN disaggregation, high‑bandwidth cross‑machine communication libraries, the TileLang programming model, RL train‑inference decoupling via SeamlessFlow, and secure, low‑latency agent infra designs for future large‑scale models.

AI infrastructureAgent SystemsGPU communication
0 likes · 27 min read
Unlocking AI Infra: Distributed Inference, PD Separation, TileLang, and Next‑Gen Agent Infrastructure
Fighter's World
Fighter's World
Dec 26, 2025 · Industry Insights

Where Is AI Heading in 2026 After the 2025 Sprint?

The article analyzes the rapid weekly turnover of leading LLM benchmarks in 2025, declining compute costs, the shift from chatbots to multi‑step agents, the widening pilot‑to‑production gap, and predicts that 2026 will be defined by infrastructure constraints, AI‑first product design, and accelerated enterprise adoption.

AI Product StrategyAI TrendsAI infrastructure
0 likes · 25 min read
Where Is AI Heading in 2026 After the 2025 Sprint?
HyperAI Super Neural
HyperAI Super Neural
Dec 19, 2025 · Artificial Intelligence

Weekly AI Paper Digest: Open-Source LLMs, Agent Systems, and Long-Context Reasoning

This week’s AI paper roundup reviews six recent research works—including RecGPT‑V2, Nemotron 3 Nano, FrontierScience benchmark, AutoGLM, Deeper‑GXX, and QwenLong‑L1.5—highlighting advances in large‑language‑model‑driven recommendation, Mixture‑of‑Experts models, expert‑level scientific reasoning, GUI‑based foundation agents, graph neural network deepening, and ultra‑long‑context inference.

AI researchAgent SystemsLarge Language Models
0 likes · 6 min read
Weekly AI Paper Digest: Open-Source LLMs, Agent Systems, and Long-Context Reasoning
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 15, 2025 · Artificial Intelligence

How to Build Robust Function Call Training Data for LLM Agents

This article explains why function call capabilities in large language model agents require dedicated training, outlines the four core abilities to teach, describes the structure and sources of effective training data, and compares lightweight LoRA fine‑tuning with full supervised fine‑tuning approaches.

Agent SystemsLLM trainingdata generation
0 likes · 11 min read
How to Build Robust Function Call Training Data for LLM Agents
DevOps
DevOps
Apr 22, 2025 · Artificial Intelligence

How to Think About Agent Frameworks: A Critical Review of Design Patterns, Challenges, and LangGraph

This article critically examines popular agent frameworks, compares OpenAI and Anthropic definitions, highlights the core difficulty of maintaining proper context for reliable agents, and presents LangGraph’s declarative and imperative features along with practical guidance for building production‑grade agent systems.

AI engineeringAgent SystemsLangGraph
0 likes · 24 min read
How to Think About Agent Frameworks: A Critical Review of Design Patterns, Challenges, and LangGraph