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Context Engineering

270 articles · Page 2 of 3
AI Large Model Application Practice
AI Large Model Application Practice
Jun 22, 2026 · Artificial Intelligence

8 Crucial Questions to Understand Loop Engineering and the New Agent Paradigm

The article breaks down Loop Engineering—a new paradigm for AI agents—by exploring why it emerged, defining its scope, distinguishing it from Agent Loops and Context/Harness Engineering, detailing its building blocks, tools, applicability criteria, and the risks and limitations of fully autonomous loops.

AI AgentsContext EngineeringHarness engineering
0 likes · 16 min read
8 Crucial Questions to Understand Loop Engineering and the New Agent Paradigm
AndroidPub
AndroidPub
Jun 22, 2026 · Artificial Intelligence

Loop Engineering: The Fourth Paradigm Shift Driving AI Agent Systems

The article traces four evolutionary jumps in AI engineering—from Prompt to Context, Harness, and finally Loop Engineering—explaining how Loop Engineering replaces manual prompting with self‑driving closed‑loop systems, outlines its five‑module architecture, memory layer, and the four conditions and safeguards needed for production‑grade AI agents.

AI AgentsContext EngineeringLoop engineering
0 likes · 14 min read
Loop Engineering: The Fourth Paradigm Shift Driving AI Agent Systems
SpringMeng
SpringMeng
Jun 21, 2026 · Artificial Intelligence

What Is the Viral “Loop” Everyone’s Talking About?

The article explains the AI‑Agent “Loop” concept that has gone viral, contrasting it with traditional programming loops, detailing the ReAct paradigm, single‑agent vs. multi‑agent loops, the four engineering layers of Prompt, Context, Loop and Harness, and discussing Loop engineering’s building blocks, benefits, limitations, and practical use cases.

AI AgentsContext EngineeringLoop engineering
0 likes · 18 min read
What Is the Viral “Loop” Everyone’s Talking About?
FunTester
FunTester
Jun 19, 2026 · Artificial Intelligence

How claude‑mem Gives Claude Code Long‑Term Project Memory

The article analyzes why Claude Code forgets project context across sessions, explains the limitations of short‑term AI chat windows, and shows how the claude‑mem tool extracts, compresses, and re‑injects essential project experience to provide high‑signal long‑term memory for safer, more context‑aware development and testing.

AI AgentsAI coding assistantClaude Code
0 likes · 11 min read
How claude‑mem Gives Claude Code Long‑Term Project Memory
JavaGuide
JavaGuide
Jun 17, 2026 · Artificial Intelligence

What’s the Difference Between Claude Code’s /loop and /goal Commands? An Interview‑Style Deep Dive

Loop Engineering repackages existing concepts like Agent Loop, ReAct, and Workflow Graph, focusing on how Claude Code’s /loop and /goal commands enable autonomous, token‑aware agent cycles with defined triggers, goals, context, verification, and stop conditions, while highlighting practical design patterns, risks, and best‑practice examples.

AI AgentsClaude CodeContext Engineering
0 likes · 26 min read
What’s the Difference Between Claude Code’s /loop and /goal Commands? An Interview‑Style Deep Dive
FunTester
FunTester
Jun 17, 2026 · Artificial Intelligence

Why Context Engineering Beats Prompt Engineering for Strong AI Agents

The article argues that in the AI Agent era, success depends less on clever prompts and more on designing high‑quality, just‑in‑time context systems, proper tool interfaces, external memory, and sub‑agent architectures to manage the model's limited attention budget.

AI AgentContext EngineeringJust-in-Time Context
0 likes · 16 min read
Why Context Engineering Beats Prompt Engineering for Strong AI Agents
Tech Minimalism
Tech Minimalism
Jun 17, 2026 · Artificial Intelligence

Why Prompt Tuning Isn’t Enough: Mastering Harness Engineering for Reliable AI Agents

The article explains that as AI agents grow more capable, merely tweaking prompts or adding context fails to ensure stable long‑term performance; instead, a systematic Harness Engineering layer that enforces constraints, validates actions, and automates feedback is essential for reliable agent operation.

AI AgentsContext EngineeringHarness engineering
0 likes · 14 min read
Why Prompt Tuning Isn’t Enough: Mastering Harness Engineering for Reliable AI Agents
AI Engineer Programming
AI Engineer Programming
Jun 16, 2026 · Artificial Intelligence

Why AI Agents Enhance, Not Replace, Code Review Workflows

The article analyzes how AI agents improve code review by using multi‑step reasoning, context engineering, graph‑based code understanding, hybrid LLM‑static analysis, and multi‑agent orchestrator‑worker architectures, while discussing design challenges, open‑source implementations, and inherent limitations.

AI AgentsContext EngineeringLLM
0 likes · 14 min read
Why AI Agents Enhance, Not Replace, Code Review Workflows
Linyb Geek Road
Linyb Geek Road
Jun 16, 2026 · Artificial Intelligence

Loop Engineering: The Next Evolution Beyond Harness Engineering in AI Coding

The article introduces Loop Engineering as a new AI coding paradigm that builds on Harness Engineering, explains its primitives, contrasts it with cron‑style automation, outlines suitable use cases, and provides a practical checklist for engineers to adopt reliable, context‑aware agent loops.

AI codingContext EngineeringLoop engineering
0 likes · 15 min read
Loop Engineering: The Next Evolution Beyond Harness Engineering in AI Coding
AI Code to Success
AI Code to Success
Jun 15, 2026 · Artificial Intelligence

Loop Engineering: When AI Starts Running Its Own Loops, What Should Engineers Do?

The article traces the evolution from Prompt Engineering to Context and Harness Engineering, introduces Loop Engineering as the next stage where AI runs autonomous cycles, explains its components, benefits, limitations, and offers guidance on when and how developers should adopt it.

AI engineeringContext EngineeringHarness engineering
0 likes · 14 min read
Loop Engineering: When AI Starts Running Its Own Loops, What Should Engineers Do?
Data Party THU
Data Party THU
Jun 14, 2026 · Artificial Intelligence

Stop Misunderstanding AI Agents: A Clear Guide to All Core Concepts

The article defines AI Agent as a system centered on a large model that can invoke tools, receive feedback, and continuously accomplish tasks, and systematically distinguishes related terms such as Model, Scaffolding, Harness, Context Engineering, Policy, Tool, Skill, Sub‑agent, Environment, Rollout, Reward, and Trainer, using concrete examples to clarify each.

AI AgentAgent TrainingContext Engineering
0 likes · 10 min read
Stop Misunderstanding AI Agents: A Clear Guide to All Core Concepts
Java Tech Enthusiast
Java Tech Enthusiast
Jun 13, 2026 · Artificial Intelligence

Why Bigger 1M‑Token Windows Still Need Careful Context Engineering

Even though modern LLMs like DeepSeek‑V4, GPT‑5.5 and Claude Opus 4.7 support 1 million‑token windows, simply stuffing more data does not improve agent performance; effective Context Engineering—selecting, structuring, and managing the right information—remains essential for reliable results.

Context EngineeringLLM agentsMemory
0 likes · 32 min read
Why Bigger 1M‑Token Windows Still Need Careful Context Engineering
Su San Talks Tech
Su San Talks Tech
Jun 13, 2026 · Artificial Intelligence

What Is the Hot New “Loop” Concept in AI Agents?

The article explains the AI‑Agent “Loop” concept—how it differs from traditional programming loops, its ReAct reasoning‑acting cycle, the full agent execution pipeline, single‑agent versus multi‑agent collaboration, engineering layers from Prompt to Harness, and practical advantages, limitations, and use cases.

AI AgentsContext EngineeringHarness²
0 likes · 17 min read
What Is the Hot New “Loop” Concept in AI Agents?
DataFunSummit
DataFunSummit
Jun 12, 2026 · Artificial Intelligence

How Ontology‑Driven Harness Engineering Enables Controllable AI Agent Execution

The article analyzes why current AI agents often act unpredictably in complex enterprises, proposes an ontology‑driven Harness Engineering framework that embeds multi‑dimensional safety constraints, context engineering, and feedback loops, and demonstrates its practical implementation through the Knora platform and a real‑world work‑order change example.

AI AgentsContext EngineeringHarness engineering
0 likes · 18 min read
How Ontology‑Driven Harness Engineering Enables Controllable AI Agent Execution
PMTalk Product Manager Community
PMTalk Product Manager Community
Jun 11, 2026 · Product Management

Why Precise Requirement Descriptions Are the New Most Valuable Skill for Product Managers in the VibeCoding Era

The article explains that while AI tools like VibeCoding can quickly generate functional code, the quality and safety of the output depend entirely on a product manager's ability to write precise, context‑rich requirement documents, covering user scenarios, success criteria, failure handling, and security boundaries.

AIContext EngineeringProduct Management
0 likes · 16 min read
Why Precise Requirement Descriptions Are the New Most Valuable Skill for Product Managers in the VibeCoding Era
JavaGuide
JavaGuide
Jun 8, 2026 · Artificial Intelligence

CodeGraph Slashes Token Usage by 59% for Claude Code and Codex

CodeGraph builds a local code knowledge graph with tree‑sitter and SQLite, allowing Claude Code, Codex and similar AI tools to query a pre‑indexed graph instead of repeatedly scanning files, which the author’s benchmarks show reduces token consumption by 59%, cuts cost 35%, speeds responses by 49% and improves routing detection for many web frameworks.

AI code toolsBackend DevelopmentCodeGraph
0 likes · 11 min read
CodeGraph Slashes Token Usage by 59% for Claude Code and Codex
SuanNi
SuanNi
Jun 6, 2026 · Artificial Intelligence

Demystifying Harness, Scaffold, and Other Tricky AI Agent Terms

This article breaks down the core terminology of AI agents—Model, Scaffold, Harness, Context Engineering, Policy, Tool Use, Skills, Sub‑agents, and the training‑side concepts of RL Environment, Trainer, Rollout, and Reward—explaining their roles, differences, and how they combine to form functional agents.

AI AgentContext EngineeringHarness²
0 likes · 12 min read
Demystifying Harness, Scaffold, and Other Tricky AI Agent Terms
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 3, 2026 · Artificial Intelligence

AI Agent Explained: From Models and Tools to Skills and Harness Engineering

This article clarifies the core concepts of AI agents, distinguishing models from agents, defining scaffolding and harness, and detailing the roles of context engineering, policy, tools, skills, sub‑agents, and training components such as environment, rollout, reward, and trainer.

AI AgentContext EngineeringLLM
0 likes · 11 min read
AI Agent Explained: From Models and Tools to Skills and Harness Engineering
DaTaobao Tech
DaTaobao Tech
Jun 1, 2026 · Artificial Intelligence

Designing LLM‑Friendly Architecture: What Truly Makes an AI‑Friendly System?

The article analyzes how traditional deterministic engineering architectures clash with the probabilistic, semantic, and dynamic nature of LLM‑driven AI, proposing three paradigm shifts and detailing an AI‑Friendly stack—including Multi‑Agent, Context Engineering, and observability—that achieved 95.7% audit accuracy and over 80% efficiency gains in real‑world marketing scenarios.

AI architectureContext EngineeringLLM
0 likes · 25 min read
Designing LLM‑Friendly Architecture: What Truly Makes an AI‑Friendly System?
DataFunTalk
DataFunTalk
May 31, 2026 · Artificial Intelligence

The Most Comprehensive Survey of Agent Harness Engineering

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.

Context EngineeringETCLOVGObservability
0 likes · 12 min read
The Most Comprehensive Survey of Agent Harness Engineering
Linyb Geek Road
Linyb Geek Road
May 31, 2026 · Artificial Intelligence

From Prompt to Harness: The Three Evolutions of AI Engineering

The article traces AI engineering's three-stage evolution—from single‑turn Prompt Engineering, through multi‑turn Context Engineering, to system‑level Harness Engineering—explaining the problems each stage solves, the techniques introduced, concrete examples, and why the shift matters for scalable, reliable AI agents.

AI engineeringAgentContext Engineering
0 likes · 11 min read
From Prompt to Harness: The Three Evolutions of AI Engineering
Data Party THU
Data Party THU
May 30, 2026 · Artificial Intelligence

The Most Comprehensive Survey of Agent Harness Engineering Revealed

This article summarizes the extensive “Agent Harness Engineering: A Survey” paper, detailing how moving beyond prompt engineering to a seven‑layer harness framework (ETCLOVG) is crucial for reliable, production‑grade agents, and explains benchmark gains, evaluation shifts, and the evolving competition from framework to platform.

AI AgentsContext EngineeringETCLOVG
0 likes · 13 min read
The Most Comprehensive Survey of Agent Harness Engineering Revealed
DataFunTalk
DataFunTalk
May 29, 2026 · Artificial Intelligence

From Prompt to Context to Harness: Unpacking the Three Paradigm Shifts in Agent Engineering

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.

AI AgentsAgent EngineeringContext Engineering
0 likes · 14 min read
From Prompt to Context to Harness: Unpacking the Three Paradigm Shifts in Agent Engineering
Linyb Geek Road
Linyb Geek Road
May 28, 2026 · Artificial Intelligence

How Harness Engineering Turns AI‑Generated Code into Enterprise‑Ready Solutions

The article analyzes why AI agents often fail in production, distinguishes Agent Harness from Harness Engineering, outlines the three pillars of Harness Engineering, compares Vibe Coding, Spec Coding and Harness Engineering, and examines real‑world implementations by Salesforce, SAP and UiPath.

AI AgentAgentic AIContext Engineering
0 likes · 28 min read
How Harness Engineering Turns AI‑Generated Code into Enterprise‑Ready Solutions
DataFunSummit
DataFunSummit
May 27, 2026 · Artificial Intelligence

How Baidu’s “Sheng Suan” Turns Agents from Outsiders into Business‑Savvy Assistants

The article explains that most AI agents achieve only 80‑90% accuracy in read‑only tasks and cannot handle core production decisions, then details Baidu’s “Sheng Suan” platform which uses a three‑layer business ontology and system‑engineered sandbox, audit, and simulation features to enable agents to execute write operations, citing three real‑world cases where decision latency dropped from months to minutes and accuracy exceeded 95%.

AI AgentsCase StudiesContext Engineering
0 likes · 8 min read
How Baidu’s “Sheng Suan” Turns Agents from Outsiders into Business‑Savvy Assistants
ArcThink
ArcThink
May 27, 2026 · Artificial Intelligence

Why Context Size Isn’t Everything: A Practical Guide to AI Agent Context Engineering

The article explains that an AI agent’s performance hinges not on how much context is fed to it but on what information is placed in the main thread, and it presents a six‑layer framework—prompt, rule files, skills, MCP, subagents, and artifacts—to systematically engineer context for reliable coding agents.

AI AgentsContext EngineeringMCP
0 likes · 18 min read
Why Context Size Isn’t Everything: A Practical Guide to AI Agent Context Engineering
Architect
Architect
May 26, 2026 · Artificial Intelligence

Why Claude’s 17 New Capabilities Matter: Moving Agents from Chat to Real Workflows

Claude’s latest suite of 17 capabilities—Projects, Memory, Artifacts, Chrome, Cowork, Skills, and more—reframes the agent from a simple chat assistant into a workflow component, prompting architects to evaluate context entry, auditable outputs, permission boundaries, repeatable processes, and cost controls before deployment.

AI AgentsArtifactsClaude
0 likes · 26 min read
Why Claude’s 17 New Capabilities Matter: Moving Agents from Chat to Real Workflows
Java Tech Enthusiast
Java Tech Enthusiast
May 26, 2026 · Artificial Intelligence

Why Interviewers Should Ask About Harness Engineering – Distinguishing It from Prompt and Context Engineering

The article explains how AI is evolving from simple chat interactions to production‑grade workflows by progressing through Prompt Engineering, Context Engineering, and finally Harness Engineering, detailing their distinct goals, practical examples, step‑by‑step processes, and why Harness is essential for building controllable, auditable AI systems.

AI workflowContext EngineeringHarness engineering
0 likes · 21 min read
Why Interviewers Should Ask About Harness Engineering – Distinguishing It from Prompt and Context Engineering
AI Waka
AI Waka
May 26, 2026 · Operations

Why a Japanese Accounting Firm’s Most Critical “Employee” Is a Markdown File

A Japanese tax accountant runs a 60‑client practice without any staff by using a simple CLAUDE.md file to orchestrate AI‑driven accounting workflows, illustrating how domain experts can translate their work into structured, safe, and automated processes that run overnight.

AI safetyAccounting automationClaude AI
0 likes · 14 min read
Why a Japanese Accounting Firm’s Most Critical “Employee” Is a Markdown File
DataFunTalk
DataFunTalk
May 25, 2026 · Artificial Intelligence

From Checks to 10‑Second Zero‑Error: Baidu Shengsu Enables AI Core Business in Shenzhen Grid

The article describes how Baidu's AI platform Shengsu, using context and system engineering, transforms smart inspection robots in Shenzhen Power Grid to automatically detect, assess, and handle equipment defects within seconds, boosting accuracy to 99% and cutting manual effort by half, illustrating AI's move from edge assistance to core production.

AI AgentBaiduContext Engineering
0 likes · 7 min read
From Checks to 10‑Second Zero‑Error: Baidu Shengsu Enables AI Core Business in Shenzhen Grid
Code Mala Tang
Code Mala Tang
May 23, 2026 · Artificial Intelligence

By 2026, AI Programming Rewards Context Management Over Pure Coding

The article argues that as AI coding agents evolve from autocomplete to task‑level assistants, developers’ most valuable skill shifts from writing code to orchestrating context, breaking down tasks, defining boundaries, and managing agents within the software production workflow.

AI programmingCoding AgentsContext Engineering
0 likes · 11 min read
By 2026, AI Programming Rewards Context Management Over Pure Coding
Tencent Tech
Tencent Tech
May 20, 2026 · Artificial Intelligence

The Three Evolutions of AI Engineering: Prompt, Context, and Harness

This article analyzes the progressive stages of AI‑driven software engineering—Prompt Engineering, Context Engineering, and Harness Engineering—illustrating how each addresses specific challenges, presenting real‑world experiments from OpenAI and Anthropic, and outlining a roadmap for engineers to master the new paradigm.

AI AgentsContext EngineeringHarness engineering
0 likes · 19 min read
The Three Evolutions of AI Engineering: Prompt, Context, and Harness
AI Large Model Application Practice
AI Large Model Application Practice
May 20, 2026 · Artificial Intelligence

Why AI Coding Falls Short of Its Promised Efficiency in Complex Enterprise Systems

Although AI coding agents like Claude Code and Codex promise dramatic productivity gains, the article explains that in large‑scale enterprise software the benefits are limited by unclear requirements, extensive context engineering, hidden token and rework costs, subtle bugs that pass superficial tests, and the need for strict risk‑tiered usage and human‑AI collaboration.

AI codingContext EngineeringROI
0 likes · 18 min read
Why AI Coding Falls Short of Its Promised Efficiency in Complex Enterprise Systems
Shi's AI Notebook
Shi's AI Notebook
May 18, 2026 · Artificial Intelligence

Anthropic’s Practical Approach to Context Engineering for AI Agents

The article explains how Anthropic engineers treat the limited token budget of large language models as a finite resource, detailing static configuration, runtime retrieval, and long‑task strategies such as compaction, structured notes, and sub‑agent architectures to build reliable, efficient AI agents.

AI AgentsAnthropicCompaction
0 likes · 18 min read
Anthropic’s Practical Approach to Context Engineering for AI Agents
Architecture and Beyond
Architecture and Beyond
May 15, 2026 · R&D Management

Managing AI Coding: The Same Principles as Leading a Development Team

The article analyses how AI‑assisted coding turns software projects into black‑boxes, explains why responsibility shifts from code authors to managers, and proposes a four‑layer control framework—requirements, architecture, implementation, and release—to keep AI output reliable, auditable, and safe.

AI codingContext EngineeringSoftware Engineering
0 likes · 22 min read
Managing AI Coding: The Same Principles as Leading a Development Team
Architect
Architect
May 10, 2026 · Artificial Intelligence

Long‑Running Agents: From Ralph Loop to Hand‑over‑Ready Harness

The article analyzes the challenges of long‑running AI agents, showing that persistence alone is insufficient and that reliable hand‑over requires explicit specifications, external state files, drift mitigation, sub‑agents, and a verifiable evidence chain to keep the work understandable for the next model or human.

AI AgentsContext EngineeringHarness²
0 likes · 25 min read
Long‑Running Agents: From Ralph Loop to Hand‑over‑Ready Harness
AI Architecture Hub
AI Architecture Hub
May 10, 2026 · Artificial Intelligence

2026 AI Engineer Roadmap: Master Agent Engineering and Scheduling

This guide outlines a six‑stage, 17‑week roadmap for becoming a production‑ready AI agent engineer by 2026, detailing essential skills such as LangGraph orchestration, Claude Agent SDK scheduling, context‑engineering primitives, evaluation pipelines, and curated free resources while warning against over‑hyped frameworks.

AI engineeringAgentic SystemsClaude Agent SDK
0 likes · 18 min read
2026 AI Engineer Roadmap: Master Agent Engineering and Scheduling
Architect
Architect
May 8, 2026 · Artificial Intelligence

From Code Retrieval to Context Operations: The Next Architecture Shift in AI Programming

The article argues that AI programming is moving from asking whether models can write code to whether agents can autonomously locate, read, modify, execute, and verify context within real engineering environments, emphasizing the migration of context control from pre‑processing pipelines to agentic loops and the need for a robust harness.

AI codingAgentic SearchClaude Code
0 likes · 22 min read
From Code Retrieval to Context Operations: The Next Architecture Shift in AI Programming
DataFunTalk
DataFunTalk
May 4, 2026 · Artificial Intelligence

Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents

The article analyzes why current AI agents lack reliable control, defines a multi‑dimensional safety framework, and proposes an ontology‑driven architecture—implemented in the Knora platform—that embeds business rules directly into agents, enabling deterministic validation, auditability, and large‑scale efficiency gains.

AIAgentBusiness Control
0 likes · 17 min read
Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents
PMTalk Product Manager Community
PMTalk Product Manager Community
May 4, 2026 · Product Management

2026 AI Product Manager: The Essential Capability Model

By 2026, AI product managers must shift from merely using models to delivering stable, valuable results, mastering seven core abilities—demand judgment, evaluation-driven iteration, context design, RAG strategy, agent orchestration, solution planning, and rapid Vibe Coding—to close the loop between business needs and AI capabilities.

AI Product ManagementContext EngineeringProduct solution
0 likes · 13 min read
2026 AI Product Manager: The Essential Capability Model
PMTalk Product Manager Community
PMTalk Product Manager Community
May 3, 2026 · Product Management

Why Every AI Product Manager Must Master Agent Architecture

Product managers are increasingly anxious about how AI Agents will reshape product logic, prompting a shift from button‑driven apps to intent‑driven assistants; this article breaks down the four core modules of an Agent, illustrates a market‑analysis workflow, and outlines design trade‑offs and common pitfalls.

AI AgentActorContext Engineering
0 likes · 11 min read
Why Every AI Product Manager Must Master Agent Architecture
AI Architecture Hub
AI Architecture Hub
May 3, 2026 · Artificial Intelligence

What to Learn, Build, and Skip in AI Agents

The article analyzes the fast‑changing AI‑agent landscape, proposes five concrete criteria for filtering new technologies, outlines essential concepts such as context engineering, tool design, scheduler‑subagent patterns, evaluation frameworks, and recommends a stable 2026 tech stack while warning against hype‑driven tools.

AI AgentsContext EngineeringLangGraph
0 likes · 27 min read
What to Learn, Build, and Skip in AI Agents
dbaplus Community
dbaplus Community
May 2, 2026 · Artificial Intelligence

Inside Claude Code: A Deep Dive into Its Powerful Agent Architecture

This article provides a comprehensive technical analysis of Claude Code, covering its project scale, two‑layer agent loop design, five design principles, tool system architecture, fine‑grained permission model, multi‑agent collaboration strategies, context‑engineered system prompts, and a custom React‑based terminal UI, all illustrated with concrete code excerpts and diagrams.

AI AgentClaude CodeContext Engineering
0 likes · 38 min read
Inside Claude Code: A Deep Dive into Its Powerful Agent Architecture
DataFunSummit
DataFunSummit
Apr 24, 2026 · Artificial Intelligence

How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering

The article analyzes why current AI agents often act unpredictably, defines a multi‑dimensional notion of safe and controllable execution, proposes an ontology‑driven semantic foundation with architecture constraints, context engineering, and feedback loops, and demonstrates the Knora implementation through concrete workflow examples.

AI AgentContext EngineeringKnora
0 likes · 20 min read
How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering
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
Sohu Tech Products
Sohu Tech Products
Apr 22, 2026 · Artificial Intelligence

What Is Harness Engineering and How to Use It in Your Projects?

Harness Engineering, the set of systems that surround and extend a large‑language‑model‑based agent, determines real‑world performance far more than the model itself, and mastering its six‑layer architecture, bottlenecks, and practical rollout steps is essential for AI‑agent development and interview preparation.

AI AgentsAgent ArchitectureContext Engineering
0 likes · 20 min read
What Is Harness Engineering and How to Use It in Your Projects?
Yunqi AI+
Yunqi AI+
Apr 21, 2026 · Artificial Intelligence

Building a Sustainable AI‑First Delivery Pipeline: Context Engineering, Skill‑as‑Code, and Governance

The article explains how AI‑first teams can turn AI coding from a hit‑or‑miss practice into a controllable, measurable engineering process by focusing on context quality, a six‑stage delivery loop, Skill‑as‑Code assets, and continuous verification, ultimately stabilizing first‑draft accuracy.

AIAgentic HarnessContext Engineering
0 likes · 22 min read
Building a Sustainable AI‑First Delivery Pipeline: Context Engineering, Skill‑as‑Code, and Governance
DataFunSummit
DataFunSummit
Apr 20, 2026 · Artificial Intelligence

Why Ontology‑Driven Agents Are the Key to Safe, Controllable Enterprise AI

The article analyses the current hype around AI agents, explains why pure prompt‑based constraints fail in complex business scenarios, and proposes an ontology‑driven Harness Engineering framework that embeds architectural constraints, context engineering, and a traceable feedback loop to achieve secure, business‑level controllability.

AI AgentsContext EngineeringEnterprise AI
0 likes · 21 min read
Why Ontology‑Driven Agents Are the Key to Safe, Controllable Enterprise AI
Baidu Maps Tech Team
Baidu Maps Tech Team
Apr 20, 2026 · Artificial Intelligence

How Baidu Maps Reinvents LBS Search with Multi‑Agent AI and RL

Facing the shift from keyword indexing to generative AI, Baidu Maps overhauled its LBS architecture by introducing a native multi‑agent system, context‑engineering (ACE) framework, and reinforcement‑learning alignment, enabling dynamic routing, knowledge evolution, and a 36% boost in planning compliance while maintaining zero‑tolerance for factual errors.

AI AgentsContext EngineeringLLM
0 likes · 10 min read
How Baidu Maps Reinvents LBS Search with Multi‑Agent AI and RL
Architecture Musings
Architecture Musings
Apr 17, 2026 · Artificial Intelligence

Building AI‑Native Design Docs with Architecture as Code

The article explains why traditional diagram‑as‑code approaches fall short for AI agents, introduces Architecture as Code as a structured, version‑controlled alternative, discusses the role of interface contracts and narrative documentation, and outlines the need for deterministic context engineering to make design documents truly live for both humans and machines.

AI AgentsContext EngineeringDiagram as Code
0 likes · 22 min read
Building AI‑Native Design Docs with Architecture as Code
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 16, 2026 · Artificial Intelligence

Why AI Product Managers Must Master Agent Architecture

The article explains how AI agents are reshaping product logic, breaks down the four core modules—Planner, Memory, Actor, and Tools—illustrates their interaction with a real‑world market‑report example, and offers design guidelines and pitfalls for product managers transitioning to intelligent, autonomous systems.

AI AgentsAgent ArchitectureAutonomous Systems
0 likes · 11 min read
Why AI Product Managers Must Master Agent Architecture
Big Data and Microservices
Big Data and Microservices
Apr 16, 2026 · Artificial Intelligence

Why Perfect Prompts Crash After Days: Uncovering the Limits of Context Engineering

An AI‑driven customer‑service bot that answered perfectly for two days suddenly started hallucinating because single‑turn prompt engineering ignored the continuous, stateful nature of real‑world conversations, revealing the hidden token, memory, and retrieval challenges that demand a new context‑engineering approach.

Context EngineeringConversation StateLLM
0 likes · 14 min read
Why Perfect Prompts Crash After Days: Uncovering the Limits of Context Engineering
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 SystemsContext EngineeringHarness engineering
0 likes · 31 min read
Why Harness Engineering Is the Next Evolution in AI System Design
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 14, 2026 · Artificial Intelligence

Why Harness Is the Strategic Asset for AI Agents in 2026

The article analyzes the 2026 turning point where AI model intelligence plateaued and argues that mastering Harness—an infrastructure that wraps models—has become the decisive factor for building controllable, scalable Agent systems, tracing its necessity through three decades of software engineering evolution.

AI AgentsClaude CodeContext Engineering
0 likes · 20 min read
Why Harness Is the Strategic Asset for AI Agents in 2026
JavaGuide
JavaGuide
Apr 14, 2026 · Artificial Intelligence

Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection

The article explains how industrial‑grade AI agents require structured prompt engineering, chain‑of‑thought reasoning, task decomposition, and a three‑layer defense (sandbox, prompt isolation, and human approval) to prevent prompt‑injection attacks, while also covering context engineering, retrieval‑augmented generation, and tool design best practices.

Context EngineeringLLM securityagent design
0 likes · 23 min read
Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection
DeepNoMind
DeepNoMind
Apr 14, 2026 · Artificial Intelligence

Harness Revolution: A Comprehensive 2026 Overview of Agent Engineering

In 2026, as large‑model capabilities plateau, the article argues that Harness—strategic infrastructure that wraps models—has become the decisive factor for successful Agent systems, tracing its evolution from prompt engineering through context engineering to the emerging Harness engineering, and detailing its core components, ecosystem, and the required shift from coder to system engineer.

AgentContext EngineeringHarness²
0 likes · 19 min read
Harness Revolution: A Comprehensive 2026 Overview of Agent Engineering
Qborfy AI
Qborfy AI
Apr 14, 2026 · Artificial Intelligence

How to Give AI a Map, Not an Encyclopedia: Mastering Context Engineering

This article explains why AI can only act on information that appears in its context window, outlines the twin problems of missing data and overload, and presents a practical methodology—including AGENTS.md maps and LangChain's LocalContextMiddleware implementation—to engineer concise, searchable context for reliable agent behavior.

AIContext EngineeringLangChain
0 likes · 29 min read
How to Give AI a Map, Not an Encyclopedia: Mastering Context Engineering
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Apr 14, 2026 · Artificial Intelligence

The First Principle of Context Engineering: Mastering the “Just‑Right” Art for AGI

The article explains that as large language models approach their capacity limits, performance is now bounded by the quality of the supplied context, advocating a “just‑right” approach that balances over‑ and under‑feeding through a three‑layer architecture, dynamic context agents, and a central router to enable scalable multi‑agent AI systems.

AI architectureContext EngineeringHarness engineering
0 likes · 9 min read
The First Principle of Context Engineering: Mastering the “Just‑Right” Art for AGI
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 13, 2026 · Artificial Intelligence

How Harness Engineering Makes or Breaks AI Agents – Lessons from Hsu’s 2026 Lecture

The article explains Harness Engineering—a set of tools that control an AI agent’s cognitive framework, capability boundaries, and behavior flow—showing how proper harnesses can turn modest models into high‑performing agents, while poor harnesses cause failures, with concrete examples, benchmarks, and research citations.

AI AgentContext EngineeringHarness engineering
0 likes · 12 min read
How Harness Engineering Makes or Breaks AI Agents – Lessons from Hsu’s 2026 Lecture
AI Engineer Programming
AI Engineer Programming
Apr 13, 2026 · Artificial Intelligence

From Harness Design to Managed Agents: Anthropic’s Full‑Stack Agent Engineering

The article examines Anthropic’s evolution of AI agent infrastructure—from single‑agent loops and context compression to multi‑agent harnesses, managed sessions, sandbox isolation, and robust context engineering—highlighting design trade‑offs, performance gains, security guarantees, and practical principles for building production‑grade agents.

AI AgentsContext EngineeringObservability
0 likes · 23 min read
From Harness Design to Managed Agents: Anthropic’s Full‑Stack Agent Engineering
Tencent Cloud Developer
Tencent Cloud Developer
Apr 10, 2026 · Artificial Intelligence

From Prompt to Harness: Mastering AI Agents, Context Engineering, and Spec‑Driven Development

The author shares a two‑part deep dive into practical AI tooling, agent‑centric workflows, and emerging engineering paradigms—covering Mac toolchains, Agent usage, Prompt vs. Context Engineering, Spec‑driven and Harness engineering, and personal reflections on staying productive amid rapid model evolution.

Context EngineeringHarness engineeringMac Toolchain
0 likes · 22 min read
From Prompt to Harness: Mastering AI Agents, Context Engineering, and Spec‑Driven Development
Fun with Large Models
Fun with Large Models
Apr 9, 2026 · Artificial Intelligence

Harness Engineering: The Critical Factor That Determines AI Agent Performance

The article explains Harness Engineering, the emerging concept that moves AI agents from simple question answering to reliable task execution by adding constraints, orchestration, observation, and recovery mechanisms, and shows how it builds on Prompt and Context Engineering through layered architecture and real‑world examples from OpenAI and Anthropic.

AI AgentsAgent ArchitectureAnthropic
0 likes · 16 min read
Harness Engineering: The Critical Factor That Determines AI Agent Performance
AndroidPub
AndroidPub
Apr 9, 2026 · Artificial Intelligence

Beyond Prompting: Mastering Harness Engineering to Build Reliable LLM Applications

This article examines the evolution from Prompt Engineering to Context Engineering and finally to Harness Engineering, presenting a six‑layer architecture and practical modules that turn large language models into robust, observable, and maintainable AI systems.

AI architectureContext EngineeringHarness engineering
0 likes · 28 min read
Beyond Prompting: Mastering Harness Engineering to Build Reliable LLM Applications
AI Engineer Programming
AI Engineer Programming
Apr 9, 2026 · Artificial Intelligence

Why Powerful AI Models Still Fail: The Real Infrastructure Challenges of Agents

Despite ever‑more capable large language models, AI agents frequently stumble because enterprise data is messy, pipelines introduce errors, RAG lacks timeliness and conflict resolution, and context assembly requires dedicated ingestion, resolution, selection, decay, and inference layers, plus a harness to manage execution and governance.

AI AgentsContext EngineeringEnterprise AI
0 likes · 19 min read
Why Powerful AI Models Still Fail: The Real Infrastructure Challenges of Agents
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 8, 2026 · Artificial Intelligence

Understanding OpenClaw: Inside the AI Agent Framework Explained by Prof. Li Hongyi

In this detailed lecture, Prof. Li Hongyi of National Taiwan University dissects the OpenClaw AI Agent, explaining its system prompts, tool usage, memory handling, sub‑agents, security risks like prompt injection, and practical safeguards for deploying autonomous agents on personal computers.

AI AgentContext EngineeringOpenClaw
0 likes · 35 min read
Understanding OpenClaw: Inside the AI Agent Framework Explained by Prof. Li Hongyi
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
IT Services Circle
IT Services Circle
Apr 5, 2026 · Artificial Intelligence

Why Harness Engineering Is the Next Frontier in AI System Design

This article explains how AI engineering has evolved from Prompt Engineering to Context Engineering and now Harness Engineering, detailing each stage's challenges, core techniques, and real‑world practices that turn large language models into reliable, long‑running production systems.

Context EngineeringHarness engineeringLLM operations
0 likes · 32 min read
Why Harness Engineering Is the Next Frontier in AI System Design
AI Step-by-Step
AI Step-by-Step
Apr 5, 2026 · Artificial Intelligence

How Context Engineering Powers Dynamic Business Data Assembly for LLM Agents

The article explains why relying solely on handcrafted prompts leads to hallucinations in LLM agents and presents six concrete context‑engineering practices—XML isolation, hierarchical ordering, KV caching, vector reranking, async memory compression, and minimal few‑shot examples—illustrated with a full e‑commerce refund‑handling case study.

AgentContext EngineeringKV cache
0 likes · 10 min read
How Context Engineering Powers Dynamic Business Data Assembly for LLM Agents
JavaEdge
JavaEdge
Apr 3, 2026 · Artificial Intelligence

Why Harness Engineering Is the Next Frontier for AI Agents

This article analyzes the rise of Harness Engineering for AI agents, contrasting it with Prompt and Context Engineering, detailing how leading companies like Anthropic, OpenAI, Google DeepMind, Windsurf, and Stripe design comprehensive runtime systems, and offering practical steps for teams to build robust agent harnesses.

AI AgentsAgent ArchitectureContext Engineering
0 likes · 12 min read
Why Harness Engineering Is the Next Frontier for AI Agents
Smart Era Software Development
Smart Era Software Development
Apr 3, 2026 · Artificial Intelligence

Claude Code Deep Dive: Engineering an AI Programming Assistant and Agent Design Best Practices

This article provides a comprehensive technical analysis of Claude Code, explaining how it transforms AI programming assistants from simple code‑completion tools into autonomous agents that can read/write files, execute commands, manage context, and coordinate multiple agents, while detailing its eight core design principles, layered architecture, tool system, context engineering, state management, security model, extensibility mechanisms, and performance optimizations.

AI AgentAgent EngineeringClaude Code
0 likes · 26 min read
Claude Code Deep Dive: Engineering an AI Programming Assistant and Agent Design Best Practices
Code Archaeology
Code Archaeology
Apr 1, 2026 · Artificial Intelligence

Harness Engineering: OpenAI's System Design Approach to AI Code Maintainability

This article analyzes OpenAI's Harness Engineering blog post, which advocates rigorous system design—context engineering, architecture constraints via CI/CD gates, and entropy combat—over reliance on better models to solve AI code maintainability, while noting high implementation costs and overlooked functional verification, contrasting with Anthropic's generator-evaluator approach.

AI code maintainabilityAnthropicCI/CD quality gates
0 likes · 6 min read
Harness Engineering: OpenAI's System Design Approach to AI Code Maintainability
AI Engineer Programming
AI Engineer Programming
Mar 31, 2026 · Artificial Intelligence

How AI Agents Achieve Self‑Evolution Through Context Engineering

The article defines AI Agent self‑evolution as an autonomous loop of perception, learning, and optimization, outlines its three evolutionary levels, key characteristics, core development components, reviews leading frameworks such as EvoSkill and DGM‑Hyperagents, and discusses safety laws for controllable evolution.

AI AgentAutonomous SystemsContext Engineering
0 likes · 9 min read
How AI Agents Achieve Self‑Evolution Through Context Engineering
Lao Guo's Learning Space
Lao Guo's Learning Space
Mar 31, 2026 · Operations

Harness Engineering Best Practices: Real‑World AI Ops Lessons from 4 Companies

This article explains Harness Engineering—a methodology that lets AI agents work reliably by steering humans and automating execution—through core principles, a performance boost demonstrated by OpenAI, and detailed case studies from OpenAI, Citi, Ancestry, and Ulta Beauty, followed by a step‑by‑step adoption roadmap.

AI engineeringCI/CDContext Engineering
0 likes · 11 min read
Harness Engineering Best Practices: Real‑World AI Ops Lessons from 4 Companies
AI Waka
AI Waka
Mar 31, 2026 · Artificial Intelligence

Which Agentic Programming Framework Wins? A Deep Comparative Review

An in‑depth evaluation of five leading Agentic programming frameworks—BMAD, SpecKit, OpenSpec, GSD, and Superpowers—examines their agents, workflows, and skills, weighing strengths, limitations, and trade‑offs to guide teams in selecting the optimal tool for AI‑assisted software development.

AI developmentAgentic ProgrammingContext Engineering
0 likes · 20 min read
Which Agentic Programming Framework Wins? A Deep Comparative Review
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 30, 2026 · Artificial Intelligence

Harness Engineering: Turning AI Agents into Reliable Digital Teams

This article analyzes the emerging paradigm of Harness Engineering, explaining how prompt, context, and feedback-loop innovations enable AI agents to act as controllable, scalable digital workers, and illustrates the concept with four real‑world case studies and open‑source projects that push the limits of AI‑driven software development.

Context EngineeringGroup IntelligenceHarness engineering
0 likes · 19 min read
Harness Engineering: Turning AI Agents into Reliable Digital Teams
Code Mala Tang
Code Mala Tang
Mar 28, 2026 · Artificial Intelligence

What Is Harness Engineering? A Deep Dive into AI Agent System Design

Harness Engineering is the emerging discipline that unifies Prompt Engineering, Context Engineering, and system-level controls to create robust, maintainable AI agent pipelines, illustrated with real-world performance gains, architectural patterns, and practical guidelines for building scalable AI‑driven workflows.

AI AgentsAI productivityContext Engineering
0 likes · 12 min read
What Is Harness Engineering? A Deep Dive into AI Agent System Design
SuanNi
SuanNi
Mar 27, 2026 · Artificial Intelligence

From Prompt to World Model: The Next Evolution of Context Engineering and AI Agents

This article surveys the rapid transformation of context engineering, tracing its journey from early prompt techniques to expansive long‑context windows, multimodal Retrieval‑Augmented Generation, and the emergence of AI agents and world models, while outlining technical challenges, economic implications, and the evolving skill set required for future practitioners.

Context EngineeringRAGartificial intelligence
0 likes · 20 min read
From Prompt to World Model: The Next Evolution of Context Engineering and AI Agents
Alibaba Cloud Native
Alibaba Cloud Native
Mar 26, 2026 · Artificial Intelligence

Why Harness Engineering Is the Next Frontier for AI Agents

The article examines the emerging paradigm of Harness Engineering, tracing its roots from the industrial and information revolutions to AI, and presents four real‑world case studies that demonstrate how prompt, context, and feedback engineering can dramatically improve large‑language‑model agents while highlighting open‑source tools for building scalable, collaborative AI systems.

AIAgent EngineeringContext Engineering
0 likes · 17 min read
Why Harness Engineering Is the Next Frontier for AI Agents
AI Programming Lab
AI Programming Lab
Mar 26, 2026 · Artificial Intelligence

LLMs to the Left, Harness Engineering to the Right: Bridging the Gap

The article argues that the real bottleneck for LLM‑driven agents is not model capability but the surrounding control system—Harness Engineering—which can dramatically boost success rates, reduce failure cascades, and become the lasting moat for AI productivity.

AI OpsContext EngineeringHarness engineering
0 likes · 14 min read
LLMs to the Left, Harness Engineering to the Right: Bridging the Gap
AgentGuide
AgentGuide
Mar 24, 2026 · Artificial Intelligence

What I Learned Moving from Backend Engineering to AI Agent Development

The author, a former backend engineer turned AI Agent developer, explains how LLM uncertainty, context engineering, shifting code responsibilities, workflow standards, new failure modes, and the ReAct paradigm shape modern Agent development, and outlines tasks best suited—or unsuited—for LLMs.

AI AgentContext EngineeringLLM
0 likes · 6 min read
What I Learned Moving from Backend Engineering to AI Agent Development
Yunqi AI+
Yunqi AI+
Mar 22, 2026 · Artificial Intelligence

Designing Hierarchical Skills and Sub‑Agents for Product Development Teams

The article explains how to define, abstract, and evaluate reusable AI Skills, integrate them into collaborative workflows, and evolve them into Sub‑Agents that own continuous responsibilities, providing concrete examples, design principles, metrics, and a step‑by‑step rollout plan for product development teams.

AI AgentsContext EngineeringProduct Development
0 likes · 14 min read
Designing Hierarchical Skills and Sub‑Agents for Product Development Teams
PaperAgent
PaperAgent
Mar 22, 2026 · Artificial Intelligence

How AI Agents Like OpenClaw Turn LLMs into Autonomous Assistants

This article explains what AI agents are, how they differ from ordinary language‑model interfaces, and walks through OpenClaw’s workflow, tool usage, security challenges, memory handling, and advanced features such as sub‑agents and context compaction, offering practical insights for building safe autonomous AI systems.

AI AgentContext EngineeringOpenClaw
0 likes · 27 min read
How AI Agents Like OpenClaw Turn LLMs into Autonomous Assistants
Architect
Architect
Mar 18, 2026 · Artificial Intelligence

Why Prompt Caching Is More Than a Cost‑Saving Trick: It Shapes Agent Architecture

The article explains that Prompt Cache is not merely a way to reduce token costs, but a fundamental mechanism that forces developers to redesign the context management of long‑running AI agents, turning caching considerations into core architectural decisions.

Context EngineeringPrompt Cachinglarge language models
0 likes · 25 min read
Why Prompt Caching Is More Than a Cost‑Saving Trick: It Shapes Agent Architecture
SuanNi
SuanNi
Mar 18, 2026 · Artificial Intelligence

How the A2A Protocol Powers Multi‑Agent Collaboration for Large Language Models

This article explains the A2A (Agent‑to‑Agent) protocol, its core concepts such as discovery, task delegation, context sharing and capability delegation, and demonstrates how it extends single‑agent MCP architectures to enable scalable, secure cooperation among specialized AI agents in complex workflows.

A2AAIContext Engineering
0 likes · 10 min read
How the A2A Protocol Powers Multi‑Agent Collaboration for Large Language Models
Yunqi AI+
Yunqi AI+
Mar 18, 2026 · Artificial Intelligence

Making AI Write Production-Ready Code via Context Engineering (COLA 5.0 & Spring Boot 3.x)

The article presents a systematic context‑engineering method that feeds project‑level, file‑level and task‑level knowledge to Claude/Claude‑CLI or Cursor, turning AI from an inconsistent code generator into a reliable collaborator, and quantifies token savings and consistency gains in a COLA 5.0 + Spring Boot 3.x project.

AI codingCOLACode Consistency
0 likes · 14 min read
Making AI Write Production-Ready Code via Context Engineering (COLA 5.0 & Spring Boot 3.x)
AI Tech Publishing
AI Tech Publishing
Mar 18, 2026 · Artificial Intelligence

How Context Engineering Turns AI Agents from ‘Usable’ to ‘Highly Effective’

The article explains how organizing the prompt, tool schemas, dialogue history, and retrieved documents—collectively the context window—affects an AI agent’s decisions, introduces the concepts of Lost‑in‑the‑Middle, Thinking Tokens, tool‑response caching, compaction versus SubAgent strategies, and shows a step‑by‑step evolution that raised accuracy from 60 % to over 95 %.

AI AgentsCompactionContext Engineering
0 likes · 9 min read
How Context Engineering Turns AI Agents from ‘Usable’ to ‘Highly Effective’
AI Waka
AI Waka
Mar 13, 2026 · Artificial Intelligence

How Event‑Driven AI Agents Eliminate Manual Skill Calls

This article explains how event‑driven AI agents replace static, manually‑triggered skill lists with deterministic, context‑aware switches, detailing the shortcomings of static references, the architecture of a skill‑switch engine, file‑based activation, additional activation modes, and the resulting productivity gains.

AI AgentsContext EngineeringSkill Activation
0 likes · 10 min read
How Event‑Driven AI Agents Eliminate Manual Skill Calls
AI Tech Publishing
AI Tech Publishing
Mar 12, 2026 · Artificial Intelligence

Why Context Engineering, Not Prompt Engineering, Is the Real Hard Work in the AI Era

The article reveals that while AI tools boost code output, they degrade quality, and that most failures stem from poor context management; it argues that true engineering effort lies in building structured, progressive context architectures—akin to infrastructure—using knowledge graphs, CLAUDE.md, and agent‑driven maintenance.

AI AgentsAnthropicCLAUDE.md
0 likes · 14 min read
Why Context Engineering, Not Prompt Engineering, Is the Real Hard Work in the AI Era
AI Waka
AI Waka
Mar 11, 2026 · Artificial Intelligence

Why Context Engineering Is the Secret to Smarter AI Agents

The article explains how context engineering—designing the entire information environment for large language models—overcomes prompt engineering limits, mitigates context decay, and improves speed, accuracy, and cost by strategically selecting, compressing, ordering, isolating, and formatting context for production‑grade AI agents.

AI AgentsAWS BedrockContext Engineering
0 likes · 24 min read
Why Context Engineering Is the Secret to Smarter AI Agents
Data Party THU
Data Party THU
Mar 8, 2026 · Artificial Intelligence

6 Practical Context‑Engineering Techniques to Tame RAG Hallucinations

This article explains why retrieval‑augmented generation (RAG) models often hallucinate, introduces the concept of context engineering, and details six practical techniques—including selective retrieval, context compression, hierarchical layout, dynamic query rewriting, memory management, and tool‑aware context—along with their trade‑offs and real‑world impact.

AIContext EngineeringLLM
0 likes · 23 min read
6 Practical Context‑Engineering Techniques to Tame RAG Hallucinations
DataFunSummit
DataFunSummit
Feb 26, 2026 · Artificial Intelligence

How Alibaba Cloud’s Aivis Redefines AI‑Powered Service Agents with Multi‑Agent Architecture

This article systematically explains the evolution of Alibaba Cloud’s intelligent service platform, focusing on the Aivis digital employee, its three‑layer Planner‑Reasoner‑Executor architecture, context‑engineering optimizations, multi‑agent workflow, and practical recommendations for building enterprise‑grade AI‑driven customer service solutions.

Context EngineeringDigital Employeearchitecture
0 likes · 24 min read
How Alibaba Cloud’s Aivis Redefines AI‑Powered Service Agents with Multi‑Agent Architecture
Tencent Cloud Developer
Tencent Cloud Developer
Feb 26, 2026 · Artificial Intelligence

Building a Minimalist AI Agent Framework: Theory, Architecture, and Code Walkthrough

This article explains the fundamentals of AI agents, compares major frameworks, introduces the ReAct, Plan‑and‑Execute, and Reflection paradigms, and provides a step‑by‑step Python implementation of a lightweight agent loop with LLM calls, tool execution, and context engineering, complete with usage examples and references.

AI AgentContext EngineeringLLM
0 likes · 28 min read
Building a Minimalist AI Agent Framework: Theory, Architecture, and Code Walkthrough