Tagged articles

context engineering

226 articles · Page 1 of 3

Sep 10, 2026 · Artificial Intelligence

Master WorkBuddy in Three Steps: Context, Memory & Skills for a Personalized AI Assistant

This article explains how to configure WorkBuddy using three core components—context (session-specific inputs), memory (persistent rules via MEMORY.md), and skills (specialized capabilities invoked with @)—to transform it from a generic chatbot into a personalized assistant that understands your workflows, preferences, and recurring tasks.

AI assistantMEMORY.mdMemory Management

0 likes · 7 min read

Master WorkBuddy in Three Steps: Context, Memory & Skills for a Personalized AI Assistant

Architect

Sep 10, 2026 · Artificial Intelligence

Claude Code Memory Architecture: How CLAUDE.md, Auto Memory, and Runtime Evidence Shape Agent Context

This article dissects Claude Code's memory loading chain by examining its source code, revealing how CLAUDE.md, Auto Memory (MEMORY.md), and live workspace evidence interact — and why memory files are navigation aids, not enforceable configuration.

AI Agent MemoryAgent ArchitectureAuto Memory

0 likes · 19 min read

Claude Code Memory Architecture: How CLAUDE.md, Auto Memory, and Runtime Evidence Shape Agent Context

inShocking

Sep 6, 2026 · Artificial Intelligence

AI Agent Context Engineering: From Information Organization to Reliable Execution

This article analyzes AI Agent context engineering, explaining how context, memory, RAG, and harness interact, how information is organized, compressed, and debugged, with practical examples from AgentScope source code.

AI agentAgentScopeCompression

0 likes · 39 min read

AI Agent Context Engineering: From Information Organization to Reliable Execution

Geek Labs

Sep 5, 2026 · Artificial Intelligence

Open-Source AI Agent Book: 10 Chapters, 103 Runnable Experiments

This article reviews an open-source book 'Deep Understanding of AI Agent' that structures AI Agent engineering around the formula Agent = LLM + Context + Tools, offering 10 chapters and 103 runnable Python experiments covering context engineering, memory, tool use, multi-agent collaboration, and model post-training.

AI agentLLMMCP protocol

0 likes · 11 min read

Open-Source AI Agent Book: 10 Chapters, 103 Runnable Experiments

ThinkingAgent

Sep 4, 2026 · Industry Insights

Enterprise AI's Real Moat: How Glean, Palantir, and OpenAI Build Context

This analysis compares three proven enterprise AI context-building approaches: Glean's knowledge-centric Enterprise Graph, Palantir's decision-centric Ontology, and OpenAI's task-centric Harness framework, showing how each addresses different organizational needs and why context—not models—is the lasting competitive advantage.

AI AgentsEnterprise AIGlean

0 likes · 27 min read

Enterprise AI's Real Moat: How Glean, Palantir, and OpenAI Build Context

DeepHub IMBA

Sep 2, 2026 · Artificial Intelligence

Prompt Engineering vs Loop Engineering: Hierarchy, Automation, and When to Use Each

The article distinguishes Prompt Engineering (single human-verified interactions) from Loop Engineering (automated iterative loops with testable success conditions), explains their hierarchical relationship, compares use cases, risks, and argues that Loop Engineering builds on Prompt Engineering to automate repetitive, verifiable tasks.

AI AgentsAI WorkflowAutomated Testing

0 likes · 15 min read

Prompt Engineering vs Loop Engineering: Hierarchy, Automation, and When to Use Each

ThinkingAgent

Aug 31, 2026 · Artificial Intelligence

Why Enterprise Knowledge and Context, Not Model Choice, Are the Core AI Assets

The article argues that as large language models converge in capability, the decisive factor for enterprise AI success shifts from selecting the most powerful model to building rich, up‑to‑date enterprise knowledge and context layers that enable agents to understand and act within a company's specific world.

AI InfrastructureEnterprise AIHarness Engineering

0 likes · 25 min read

Why Enterprise Knowledge and Context, Not Model Choice, Are the Core AI Assets

Fighter's World

Aug 30, 2026 · Artificial Intelligence

Uber's AI Software Factory: How Enterprises Turn Coding Agents into Measured Production

This article analyzes Uber's enterprise-scale Software Factory for AI coding agents, detailing their four-layer agent architecture, real-task benchmarking for model routing, six-factor cost decomposition, context engineering optimizations, managed agent runtimes, and a six-step framework for organizations to build their own measurable, self-improving AI development pipelines.

AI coding agentsSoftware FactoryUber

0 likes · 45 min read

Uber's AI Software Factory: How Enterprises Turn Coding Agents into Measured Production

Fighter's World

Aug 26, 2026 · Artificial Intelligence

Beyond Code Generation: Coding Agents Now Compete on AI-Native SDLC

The article argues that code generation is no longer the bottleneck; competition among coding agents is shifting toward managing the entire AI-Native software development lifecycle — Plan, Design, Build, Test, Deploy, Maintain — using versioned artifacts, automated verification, governance gates, and context engineering, as raw model capabilities become commoditized.

AI-Native SDLCAI-assisted developmentAnthropic

0 likes · 50 min read

Beyond Code Generation: Coding Agents Now Compete on AI-Native SDLC

AI Cyberspace

Aug 25, 2026 · Artificial Intelligence

Designing Harness Engineering for Enterprise Vertical Agents: From First Principles to Architecture

The article analyzes why large language model agents succeed in coding but falter in vertical production scenarios, introduces a five‑dimensional diagnostic framework and a six‑layer Harness architecture, and demonstrates its application through a production‑ops on‑call agent and an intelligent Q&A bot.

AI OpsAgentHarness Engineering

0 likes · 43 min read

Designing Harness Engineering for Enterprise Vertical Agents: From First Principles to Architecture

Software Engineering 3.0 Era

Aug 22, 2026 · Artificial Intelligence

Codex Harness: How OpenAI Built an Embeddable Agent OS in 135 Rust Crates

This article dissects OpenAI's Codex Harness, a 146K-line Rust workspace of 135 crates that powers ChatGPT, CLI, and IDE extensions as an embeddable agent platform, detailing its agent loop, context engineering, JSON-RPC protocol, multi-OS sandboxing, and multi-agent architecture with measurable benchmark gains on ARC-AGI-3.

Agent ArchitectureAgent LoopCodex Harness

0 likes · 33 min read

Codex Harness: How OpenAI Built an Embeddable Agent OS in 135 Rust Crates

The Dominant Programmer

Aug 20, 2026 · Artificial Intelligence

Deep Dive into Context Engineering with Spring AI Alibaba

This article explains how Spring AI Alibaba’s Agent framework uses Hooks and Interceptors to dynamically control model, tool, and lifecycle contexts, demonstrating the concepts through a complete e‑commerce smart‑customer‑service demo with role‑based tool selection, dynamic prompts, and context compression.

AI agentInterceptorJava

0 likes · 21 min read

Deep Dive into Context Engineering with Spring AI Alibaba

DeWu Technology

Aug 19, 2026 · Artificial Intelligence

How EP-Harness Turns Personal AI Coding into a Team‑Level Agent Workflow

The article analyzes the shortcomings of using AI coding tools individually—such as unreviewed prompts, lost experience, lack of visibility, and broken development loops—and explains how EP-Harness provides a managed‑agent platform with layered architecture, unified execution contracts, context engineering, and loop automation to turn AI agents into governed, team‑wide production assets.

AI AgentsAI codingautomation loop

0 likes · 12 min read

How EP-Harness Turns Personal AI Coding into a Team‑Level Agent Workflow

AI Large-Model Wave and Transformation Guide

Aug 18, 2026 · Artificial Intelligence

Mastering AI Context Engineering: The Four Core Components Explained

The article breaks down AI context engineering into four essential responsibilities—state manager, orchestration layer, context loader, and context assembler—illustrating how each step clarifies what to do, which data to trust, and how to feed the AI the right information for tasks like activity registration or article drafting.

AIContext AssemblyContext Loading

0 likes · 12 min read

Mastering AI Context Engineering: The Four Core Components Explained

DataFunSummit

Aug 14, 2026 · Artificial Intelligence

How Ontology‑Driven Harness Engineering Enables Controllable Agent Execution

The article analyses why current AI agents often act beyond business rules, proposes an ontology‑driven Harness Engineering framework that provides built‑in architectural constraints, context engineering, and a verifiable feedback loop, and demonstrates its practical realization through the Knora platform with real‑world case studies.

AI AgentsHarness EngineeringKnora

0 likes · 20 min read

How Ontology‑Driven Harness Engineering Enables Controllable Agent Execution

Java Tech Enthusiast

Aug 12, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code Prompts Without Dropping Performance

Anthropic removed more than 80% of the system prompts for Claude Code (Claude 5), yet benchmark scores stayed stable, prompting a deep dive into why excessive rules hindered the model, how progressive disclosure and skill modularization improve efficiency, and what developers should do with their CLAUDE.md files.

AIAnthropicClaude

0 likes · 16 min read

Why Anthropic Cut 80% of Claude Code Prompts Without Dropping Performance

Architect

Aug 10, 2026 · Artificial Intelligence

Anthropic Deep Dive: Context Engineering Lessons from Real‑World R&D

The article analyzes Anthropic’s “Effective context engineering for AI agents,” showing how larger context windows can degrade, categorizing information by stability, designing prompts in the Goldilocks zone, structuring tool contracts, and applying runtime information scheduling, compression, structured notes, and sub‑agents to keep AI agents reliable in complex development workflows.

AI AgentsAnthropicLLM

0 likes · 19 min read

Anthropic Deep Dive: Context Engineering Lessons from Real‑World R&D

AI Large Model Application Practice

Aug 10, 2026 · Artificial Intelligence

Is Graph Engineering Just Repackaged Old Tech or the Next Step for Powerful AI Agents?

The article explains that Graph Engineering does not introduce new technology but redefines how increasingly capable AI agents are organized, contrasting it with earlier Loop Engineering, outlining its core components, practical examples, and the specific scenarios where a graph‑based approach becomes essential.

AI WorkflowGraph EngineeringHarness Engineering

0 likes · 14 min read

Is Graph Engineering Just Repackaged Old Tech or the Next Step for Powerful AI Agents?

Machine Heart

Aug 9, 2026 · Artificial Intelligence

Why Continual Learning Won’t Take Ten Years—Five Hot Paths and the Fight Against Catastrophic Forgetting

The article surveys five emerging approaches to LLM continual learning—external agent memory, context engineering, post‑training, pre‑training, and self‑modifying models—explaining how each tackles the core obstacle of catastrophic forgetting, citing benchmarks such as TRACE, ACE, and SDFT, and reflecting on Karpathy’s ten‑year timeline.

Agent MemoryLLMcatastrophic forgetting

0 likes · 17 min read

Why Continual Learning Won’t Take Ten Years—Five Hot Paths and the Fight Against Catastrophic Forgetting

Tencent Technical Engineering

Aug 7, 2026 · Artificial Intelligence

From AI Gibberish to Precise Code Changes: How I Made AI Understand a Legacy Project

The article details a year‑long effort to transform a heavily indebted legacy web platform into an AI‑maintainable system by building explicit AI context, pruning dead code, simplifying architecture, establishing standards, and automating tests, ultimately reducing developer overhead and improving release stability.

AIAutomation TestingLegacy Refactoring

0 likes · 19 min read

From AI Gibberish to Precise Code Changes: How I Made AI Understand a Legacy Project

TechVision Expert Circle

Aug 3, 2026 · Artificial Intelligence

Designing an AI Auto‑Programming System That Outpaces Junior Developers

This article dissects how to build a production‑grade AI auto‑programming system—covering the tasks junior developers spend their time on, a four‑layer architecture, core modules, model tiering, context engineering, toolchain integration, multi‑stage quality checks, and current limitations.

AI programmingSoftware Architecturecode generation

0 likes · 15 min read

Designing an AI Auto‑Programming System That Outpaces Junior Developers

Machine Heart

Aug 2, 2026 · Artificial Intelligence

From TPU to Self‑Evolving Agents: Jeff Dean’s Take on AI’s Next Step

In a candid YC Startup School interview, Jeff Dean explains how AI is moving from ever‑larger models to long‑running, self‑evolving agents, emphasizing inference hardware, data‑movement costs, context engineering, and the strategic 1% rule that guides startups to focus on problems where generic models still fail.

AIStartup StrategyTPU

0 likes · 26 min read

From TPU to Self‑Evolving Agents: Jeff Dean’s Take on AI’s Next Step

AI Architecture Hub

Aug 2, 2026 · Artificial Intelligence

Why Stronger Models Need Shorter Prompts: Claude 5 Cuts 80% of System Prompts

Anthropic’s July 2026 release of Claude Opus 5 and Fable 5 demonstrates that trimming more than 80% of system prompts can maintain coding benchmark performance, revealing a shift from bulky prompt engineering to a three‑layer context architecture that assigns minimal, task‑specific information to the model.

AI agentClaude-5LLM

0 likes · 15 min read

Why Stronger Models Need Shorter Prompts: Claude 5 Cuts 80% of System Prompts

Data Party THU

Jul 30, 2026 · Artificial Intelligence

Why Deleting 80% of Claude Code System Prompts Doesn’t Hurt Performance

Thariq, a core member of Anthropic’s Claude Code team, shows that removing more than 80% of system prompts from Claude 5 models yields no measurable loss in coding benchmarks, prompting a fundamental shift in prompt‑engineering practices.

AI coding assistantsClaudeClaude Fable 5

0 likes · 10 min read

Why Deleting 80% of Claude Code System Prompts Doesn’t Hurt Performance

DataFunSummit

Jul 29, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven, Controllable Agents

The article analyses why the current wave of AI agents often “runs away” from business rules, proposes an ontology‑driven semantic base to make agents safely controllable, details three technical pillars—architecture constraints, context engineering, and feedback loops—and illustrates the Knora implementation with a concrete work‑order change workflow.

AI AgentsKnoraOntology

0 likes · 20 min read

Harness Engineering’s Semantic Foundation: Ontology‑Driven, Controllable Agents

AI Programming Lab

Jul 27, 2026 · Artificial Intelligence

Less Is More: Context Engineering Practices After Claude Opus 5 and Fable 5

The article analyzes how Claude Opus 5 and Fable 5 reshape context engineering by removing over‑constraining prompts, trimming rule files, and adopting progressive disclosure, showing six concrete before‑after rule groups and practical guidance for lightweight CLAUDE.md and Skills design.

AIClaudeFable 5

0 likes · 10 min read

Less Is More: Context Engineering Practices After Claude Opus 5 and Fable 5

PaperAgent

Jul 27, 2026 · Artificial Intelligence

Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules

Anthropic’s official Claude 5 guide reveals that removing most Claude Code system prompts has no measurable impact, overturning traditional context‑engineering practices and introducing six paradigm shifts that let the model rely on its own judgment and progressive context loading.

AI AgentsAnthropicClaude-5

0 likes · 6 min read

Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules

PaperAgent

Jul 27, 2026 · Artificial Intelligence

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

This 149‑page survey defines long‑horizon agents as a coupling of a base policy and a runtime harness (Agent = πθ ⊕ H), categorises task levels and capabilities, traces the field’s evolution from prompt to context to runtime engineering, and outlines a seven‑stage optimization pipeline, application forms, and frontier challenges, supported by empirical growth data and extensive references.

AI SurveyAgent optimizationAgentic AI

0 likes · 12 min read

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

Machine Learning Algorithms & Natural Language Processing

Jul 25, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code System Prompts Overnight

Anthropic discovered that with the release of Claude Opus 5 the previous, heavily‑engineered system prompts became unnecessary, so they removed more than 80% of Claude Code’s prompts without measurable loss, and outlined new concise, context‑driven best practices for LLM prompt engineering.

AI developmentAnthropicClaude

0 likes · 11 min read

Why Anthropic Cut 80% of Claude Code System Prompts Overnight

Tech Ocean

Jul 25, 2026 · Artificial Intelligence

Why Cutting 80% of Claude’s System Prompt Improves Performance and How to Slim Your CLAUDE.md

Anthropic removed roughly 80% of Claude Code’s system prompts without measurable loss, showing that hard‑coded rules often conflict and waste compute, and outlining new best‑practice guidelines—letting the model decide, progressive disclosure, code‑centric specifications, and a four‑layer context hierarchy.

AI modelClaudeUnhobbling

0 likes · 13 min read

Why Cutting 80% of Claude’s System Prompt Improves Performance and How to Slim Your CLAUDE.md

Machine Heart

Jul 25, 2026 · Artificial Intelligence

Why Claude Code Cut 80% of System Prompts Overnight

Anthropic discovered that after launching the stronger Claude Opus 5 model, they could remove more than 80% of Claude Code’s system prompts without any measurable loss in coding performance, prompting a shift toward minimal, high‑level context engineering that relies on concise CLAUDE.md files, Skills, and progressive disclosure.

AI developmentAnthropicClaude

0 likes · 11 min read

Why Claude Code Cut 80% of System Prompts Overnight

DataFunTalk

Jul 24, 2026 · Artificial Intelligence

Agent Harness Unpacked: A Deep Dive into AI Agent Architecture

The article dissects the concept of an Agent Harness—software infrastructure that wraps a stateless LLM to enable autonomous agent behavior—detailing its three engineering layers, twelve core components, execution loop, benchmark gains, and design trade‑offs across Anthropic, OpenAI, LangChain, LangGraph, CrewAI and AutoGen frameworks.

AI AgentsLLM infrastructureMemory Management

0 likes · 19 min read

Agent Harness Unpacked: A Deep Dive into AI Agent Architecture

DataFunTalk

Jul 23, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Dissecting the Architecture Behind AI Agents

The article explains that an Agent Harness is the full software infrastructure surrounding a large language model—handling orchestration loops, tool integration, memory, context management, state persistence, error handling, safety guards, and validation—showing why harness design, not model size, determines production‑grade agent performance.

AI AgentsLLMMemory Management

0 likes · 19 min read

Deep Dive into Agent Harness: Dissecting the Architecture Behind AI Agents

DataFunTalk

Jul 22, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Unpacking the Architecture Behind AI Agents

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.

AI AgentsLLM infrastructureMemory Management

0 likes · 20 min read

Deep Dive into Agent Harness: Unpacking the Architecture Behind AI Agents

AI Architecture Hub

Jul 18, 2026 · Artificial Intelligence

How Anthropic Boosted Engineer Code Output 8‑Fold with Context Engineering

Anthropic engineers increased daily code merges by eight times without changing models, hardware, or team size, simply by giving Claude a fully engineered context before it acts, a practice the article calls "context engineering" that replaces traditional prompt engineering.

AGENTS.mdAI AgentsClaude

0 likes · 16 min read

How Anthropic Boosted Engineer Code Output 8‑Fold with Context Engineering

AI Engineer Programming

Jul 18, 2026 · Artificial Intelligence

13 Agentic AI Trends to Watch in 2026

The article analyzes thirteen emerging Agentic AI trends for 2026—including CLI agents, the resurgence of MCP, multi‑agent orchestration, agentic commerce, AI governance, personal assistants, context engineering, vertical agents, small language models, recursive LMs, real‑time web access, browser agents, and verifiability—backed by data, case studies, and industry reports.

AI GovernanceAgentic AICLI agents

0 likes · 29 min read

DataFunTalk

Jul 15, 2026 · Artificial Intelligence

Agent Harness Unpacked: A Deep Dive into AI Agent Architecture

The article dissects the concept of an Agent Harness— the full software infrastructure that turns a stateless LLM into a capable, autonomous agent—by detailing its three engineering layers, twelve core components, execution loop, framework implementations, and the trade‑offs that determine performance, reliability, and security.

AI Agent FrameworksError HandlingLLM

0 likes · 22 min read

Linyb Geek Road

Jul 14, 2026 · Artificial Intelligence

Understanding MCP, Skill, Harness, and Loop: A Deep Dive into the Four‑Layer AI Agent Architecture

The article breaks down the four‑layer AI Agent stack—MCP protocol, Agent Skill, Harness runtime, and Loop engineering—showing how each layer solves distinct problems, presenting benchmark data (e.g., a 25.7 pp SWE‑bench gain from Harness changes), security analyses, design trade‑offs, and a production checklist.

AI Agent ArchitectureAgent SkillLoop Engineering

0 likes · 26 min read

Understanding MCP, Skill, Harness, and Loop: A Deep Dive into the Four‑Layer AI Agent Architecture

KooFE Frontend Team

Jul 13, 2026 · Artificial Intelligence

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

This article surveys the progression of AI agent engineering—from early prompt engineering focused on crafting input text, through context engineering that manages information flow, to harness engineering which builds reliable, secure agent systems—detailing definitions, techniques, limitations, and the four core modules needed for robust agents.

AI agentAgent RuntimeHarness Engineering

0 likes · 8 min read

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

TechVision Expert Circle

Jul 13, 2026 · Artificial Intelligence

Why AI Coding’s Biggest Anxiety Shifts from Accuracy to Engineering Control in Late 2026

In the second half of 2026 the AI coding debate has moved from questioning whether AI can write code to worrying about who will guarantee the safety, consistency, and security of AI‑generated code in complex engineering environments.

AI codingDevOps IntegrationMulti-Agent Systems

0 likes · 11 min read

Why AI Coding’s Biggest Anxiety Shifts from Accuracy to Engineering Control in Late 2026

DataFunTalk

Jul 12, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agents

The article analyzes why the current Agent boom suffers from uncontrolled behavior, proposes a multi‑dimensional safety framework built on ontology‑driven constraints, context engineering, and feedback loops, and demonstrates its practical realization through the Knora platform with real‑world case studies.

AI AgentsEnterprise AIKnora

0 likes · 20 min read

Data Party THU

Jul 11, 2026 · Artificial Intelligence

From Prompt to Loop: A Comprehensive 7,500‑Word Review of AI Engineering Paradigms

This article surveys the four major AI engineering paradigms—Prompt, Context, Harness, and Loop—detailing their technical logic, practical implementations, trade‑offs, and real‑world incidents, while providing concrete guidelines and comparative analysis for building autonomous AI systems.

AI AgentsHarness EngineeringLoop Engineering

0 likes · 25 min read

From Prompt to Loop: A Comprehensive 7,500‑Word Review of AI Engineering Paradigms

TonyBai

Jul 10, 2026 · Artificial Intelligence

The New AI Stack: Models, Harnesses, Loops, and Self‑Evolving Agents

The article argues that AI product performance hinges not on ever smarter foundation models but on the surrounding harness framework—covering loops, file‑system memory, sub‑agents, context engineering, and self‑optimizing code—and provides concrete patterns, pitfalls, and a four‑week roadmap for developers.

AIAgentic SystemsEvolutionary Search

0 likes · 26 min read

The New AI Stack: Models, Harnesses, Loops, and Self‑Evolving Agents

AI Engineering

Jul 8, 2026 · Artificial Intelligence

How AI Can Achieve Recursive Self‑Improvement: Lilian Weng Says Build a Robust Harness First

The article examines recursive self‑improvement in AI, arguing that a well‑designed harness—responsible for workflow orchestration, context management, and tool integration—is as crucial as model intelligence, and outlines design patterns, meta‑engineering approaches, evolutionary search methods, and the remaining challenges for truly autonomous AI systems.

AI self‑improvementAgent designEvolutionary Search

0 likes · 17 min read

How AI Can Achieve Recursive Self‑Improvement: Lilian Weng Says Build a Robust Harness First

ThinkingAgent

Jul 7, 2026 · Artificial Intelligence

Why a Single Word Change Can Cost Days: PromptOps and Context Engineering in LLM Production

The article explains how a tiny tweak in a system prompt can trigger a three‑day outage, then details the L3 context layer that organizes prompts, version‑controls them, allocates token budgets, compresses context, runs A/B tests, and compares open‑source and SaaS PromptOps platforms for reliable LLM deployments.

A/B testingLLM ProductionPrompt Versioning

0 likes · 26 min read

Why a Single Word Change Can Cost Days: PromptOps and Context Engineering in LLM Production

AI Large Model Application Practice

Jul 6, 2026 · Artificial Intelligence

20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)

This article breaks down the 20 core concepts essential for building enterprise agents in 2026, covering the agent definition, harness framework, execution models, loop engineering, state and context management, prompt caching, ontology, and live retrieval, each illustrated with practical examples and engineering tips.

AgentHarnessLLM

0 likes · 17 min read

20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)

DataFunSummit

Jul 4, 2026 · Artificial Intelligence

How Ontology‑Driven Architecture Enables Controllable AI Agents

The article analyzes the limitations of current Agent‑centric AI solutions and proposes an ontology‑driven “Harness Engineering” framework that embeds business rules directly into the semantic layer, providing architecture constraints, context engineering, and feedback loops to achieve safe, auditable, and business‑controllable agent execution.

AI agentControlKnora

0 likes · 18 min read

How Ontology‑Driven Architecture Enables Controllable AI Agents

Su San Talks Tech

Jul 4, 2026 · Artificial Intelligence

How to Write an Effective CLAUDE.md for Claude Code (Even If You’ve Only Used It 2½ Years)

The article explains why AI coding tools like Claude Code need a concise CLAUDE.md onboarding file, details what should be included, offers concrete best‑practice tips such as keeping the file under 200 lines, using positive directives, leveraging Hooks and AGENTS.md, and provides troubleshooting steps and team‑collaboration strategies.

AGENTS.mdAI programmingCLAUDE.md

0 likes · 16 min read

How to Write an Effective CLAUDE.md for Claude Code (Even If You’ve Only Used It 2½ Years)

DataFunSummit

Jul 2, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agent Execution

The article analyzes why current AI agents, despite impressive demos, often act beyond business rules, proposes an ontology‑driven semantic base called Harness Engineering to embed constraints, context, and auditability directly into the agent’s execution flow, and details the Knora implementation that demonstrates these concepts in real‑world scenarios.

AI AgentsKnoraOntology

0 likes · 19 min read

Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agent Execution

Tencent Cloud Developer

Jul 2, 2026 · Artificial Intelligence

What Is Loop Engineering? A Deep Dive into the Four‑Layer Evolution of Enterprise AI Agents

The article maps the progression from Prompt to Context, Harness, and finally Loop Engineering, explains how each layer adds new engineering dimensions for reliable enterprise AI agents, provides concrete examples, risks, industry‑specific guidance, and a step‑by‑step adoption framework.

AI OpsAI agentEnterprise AI

0 likes · 34 min read

What Is Loop Engineering? A Deep Dive into the Four‑Layer Evolution of Enterprise AI Agents

AI Engineer Programming

Jul 2, 2026 · Artificial Intelligence

Will Models Eventually Replace Harness Engineering? A Historical Analysis

The article traces the evolution of AI from early symbolic expert systems through connectionist, statistical, and deep learning eras, showing how increasingly powerful models have progressively subsumed handcrafted harnesses, and examines modern agent architectures, experimental evidence, and a six‑layer harness framework.

AIAgentHarness Engineering

0 likes · 17 min read

Will Models Eventually Replace Harness Engineering? A Historical Analysis

Xike

Jul 1, 2026 · Artificial Intelligence

How Skill Packages Turn AI Agents into Scenario Experts

The article explains how reusable Skill packages encapsulate domain knowledge, standard workflows, and output formats to enable AI agents to act professionally in specific contexts, addressing token efficiency, team conventions, and seamless integration with Loop, Harness, Context, Tool Use, and MCP.

AI AgentsPrompt designSkill module

0 likes · 15 min read

How Skill Packages Turn AI Agents into Scenario Experts

DataFunSummit

Jun 30, 2026 · Artificial Intelligence

From Prompt to Loop: A Comprehensive Review of AI Development Paradigms

The article traces the evolution of large‑language‑model engineering from early prompt engineering through context and harness engineering to the emerging loop engineering paradigm, detailing each stage’s techniques, challenges, technical debt, cost‑caching mechanisms, safety contracts, and practical guidelines for building production‑grade autonomous AI agents.

AI AgentsHarness EngineeringLoop Engineering

0 likes · 26 min read

From Prompt to Loop: A Comprehensive Review of AI Development Paradigms

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 ArchitectureHarness Engineering

0 likes · 16 min read

From Prompt to Loop: The Evolution of AI Development Paradigms

AI Tech Publishing

Jun 29, 2026 · Artificial Intelligence

Productionizing LLM Agent Harness: Architecture, Backend Design, and Optimization

The guide explains how to turn a basic LLM call into a production‑ready multi‑agent system by introducing the Agent Harness architecture—five components (Orchestrator, Subagents, Skills, Backend, Context Engineering)—and detailing backend state handling, isolated sub‑agents, caching layers, token optimization, async task queues, and observability best practices.

Async TasksCachingLLM

0 likes · 27 min read

Productionizing LLM Agent Harness: Architecture, Backend Design, and Optimization

ThinkingAgent

Jun 24, 2026 · Artificial Intelligence

Knowledge Engineering for RAG: Ontology, GraphRAG, Agentic RAG, and Context Engineering

By 2026, teams find standard RAG insufficient and turn to knowledge engineering—using Ontology to structure domain concepts, GraphRAG to add graph‑based retrieval, Agentic RAG for proactive multi‑round searching, and Context Engineering to finely manage prompts—resulting in higher relevance, lower token cost, and richer AI answers.

Agentic RAGGraphRAGKnowledge Graph

0 likes · 18 min read

Knowledge Engineering for RAG: Ontology, GraphRAG, Agentic RAG, and Context Engineering

FunTester

Jun 24, 2026 · Artificial Intelligence

What Should Claude‑mem Remember? Practical Guidelines for Effective Long‑Term Memory

The article explains that Claude‑mem’s long‑term memory should store high‑value, decision‑impacting knowledge rather than raw chat logs, outlines six categories of information worth remembering, three types to avoid, and provides concrete formats and cleanup practices to keep the memory useful for future AI‑assisted development.

AI memoryClaudeClaude-Mem

0 likes · 14 min read

What Should Claude‑mem Remember? Practical Guidelines for Effective Long‑Term Memory

DataFunTalk

Jun 23, 2026 · Artificial Intelligence

What Is an Agent Harness? A Deep Dive into AI Agent Architecture

The article dissects the concept of an Agent Harness— the full software infrastructure that surrounds large language models—explaining its layers, twelve essential components, step‑by‑step execution loop, framework implementations, and key design decisions that determine production‑grade AI agent performance.

AI AgentsLLM infrastructureMemory Management

0 likes · 21 min read

What Is an Agent Harness? A Deep Dive into AI Agent Architecture

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 AgentsAgent LoopAutomation

0 likes · 16 min read

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

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 AgentsAutomationLoop Engineering

0 likes · 14 min read

Loop Engineering: The Fourth Paradigm Shift Driving AI Agent Systems

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 AgentsLoop EngineeringMulti-Agent Collaboration

0 likes · 18 min read

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

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

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 AgentsAutomationClaude Code

0 likes · 26 min read

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

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 agentJust-in-Time ContextSub‑agents

0 likes · 16 min read

Why Context Engineering Beats Prompt Engineering for Strong AI Agents

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 AgentsHarness EngineeringLLM Operations

0 likes · 14 min read

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

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 AgentsLLMcode-review

0 likes · 14 min read

Why AI Agents Enhance, Not Replace, Code Review Workflows

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 codingAutomationLoop Engineering

0 likes · 15 min read

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

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 engineeringAutomationHarness Engineering

0 likes · 14 min read

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

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 TrainingHarness

0 likes · 10 min read

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

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.

LLM AgentsRAGSub-Agent

0 likes · 32 min read

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

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 AgentsHarnessLoop Engineering

0 likes · 17 min read

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

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 AgentsHarness EngineeringKnora

0 likes · 18 min read

How Ontology‑Driven Harness Engineering Enables Controllable AI Agent Execution

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.

AISecurityVibeCoding

0 likes · 16 min read

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

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 toolsCodeGraphLLM token reduction

0 likes · 11 min read

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

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 agentHarnessRL training

0 likes · 12 min read

Demystifying Harness, Scaffold, and Other Tricky AI Agent Terms

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 agentLLMSub-Agent

0 likes · 11 min read

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

IT Services Circle

Jun 1, 2026 · Artificial Intelligence

Why Bigger LLM Context Windows Don’t Guarantee Better Agent Performance

Even with 1‑million‑token windows in models like DeepSeek‑V4, GPT‑5.5, and Claude Opus 4.7, agents often underperform because noisy or poorly ordered context overwhelms the model, making careful Context Engineering essential for reliable results.

AI AgentsLLMMemory Management

0 likes · 30 min read

Why Bigger LLM Context Windows Don’t Guarantee Better Agent Performance

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 architectureLLMRAG

0 likes · 25 min read

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

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.

ETCLOVGEvaluationGovernance

0 likes · 12 min read

The Most Comprehensive Survey of Agent Harness Engineering

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 engineeringAgentHarness Engineering

0 likes · 11 min read

From Prompt to Harness: The Three Evolutions of AI Engineering

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 AgentsETCLOVGGovernance

0 likes · 13 min read

The Most Comprehensive Survey of Agent Harness Engineering Revealed

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 EngineeringEvaluation

0 likes · 14 min read

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

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 AIEnterprise AI

0 likes · 28 min read

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

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 AgentsEnterprise AIbusiness ontology

0 likes · 8 min read

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

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 AgentsMCPPrompt design

0 likes · 18 min read

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

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

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 WorkflowHarness EngineeringLLM Engineering

0 likes · 21 min read

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

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

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 agentBaiduPower Grid

0 likes · 7 min read

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

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

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 AgentsHarness EngineeringRetrieval-Augmented Generation

0 likes · 19 min read

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

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 codingROIcontext engineering

0 likes · 18 min read

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

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 AgentsAnthropicLLM

0 likes · 18 min read

Anthropic’s Practical Approach to Context Engineering for AI Agents

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 codingarchitectural constraintscontext engineering

0 likes · 22 min read

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

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 AgentsHarnessLong-Running Agents

0 likes · 25 min read

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

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

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

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

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 managementAgent designEvaluation

0 likes · 13 min read

2026 AI Product Manager: The Essential Capability Model

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 agentActorProduct Architecture

0 likes · 11 min read

Why Every AI Product Manager Must Master Agent Architecture