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context engineering

269 articles · Page 1 of 3
DataFunSummit
DataFunSummit
Oct 25, 2026 · Industry Insights

How Alibaba Taotian Achieved Sub-$0.30 Design-to-Code with Tarot Pixel

Alibaba Taotian's Tarot Pixel cuts design-to-code costs below 2 yuan by combining context engineering — exporting structured visual data from MasterGo — with a feedback loop that verifies generated code via screenshot diffs and render-tree comparisons, while a denoising pipeline reduces layer depth from 8–12 to 3–5 and a layout audit with 12 anti-patterns prevents overfitting.

AI-assisted codingAlibaba TaotianTarot Pixel
0 likes · 5 min read
How Alibaba Taotian Achieved Sub-$0.30 Design-to-Code with Tarot Pixel
Alibaba Cloud Native
Alibaba Cloud Native
Oct 24, 2026 · Artificial Intelligence

AgentCore: 10 Capabilities for Enterprise Agent Lifecycle Governance

This article details AgentCore, an enterprise-grade agent platform, covering its ten core capabilities: controlled execution with runtime/sandbox/tool boundaries, full-chain observability with trace metrics, context engineering unifying memory and knowledge, multi-agent team collaboration with three lifecycle modes, evaluation via Code/LLM/Agent judges, skill lifecycle management with six-stage governance, three-layer asset catalog, identity/security with triple identity and audit trails, cost FinOps with per-call metering and quotas, and full lifecycle management from build to continuous improvement.

AI AgentsAgentCoreEnterprise AI
0 likes · 12 min read
AgentCore: 10 Capabilities for Enterprise Agent Lifecycle Governance
Architect
Architect
Oct 19, 2026 · Artificial Intelligence

Why a 50-Round PR Review Reveals AI Agent Core Architecture

Analyzing a Claude Code PR that stalled after 50+ review cycles, this article breaks down AI agent architecture into a control loop with explicit stop conditions, dual data flows for context and evidence, and a three-layer system separating model judgment, runtime control, and external fact storage — showing why convergence requires engineering beyond model capabilities.

AI AgentsAgent ArchitectureAnthropic
0 likes · 27 min read
Why a 50-Round PR Review Reveals AI Agent Core Architecture
DataFunSummit
DataFunSummit
Oct 17, 2026 · Artificial Intelligence

Tencent's Digital Employees Achieve 10x Speedup: Rewritten Agent Architecture Delivers Minutes-Level Data Analysis

Tencent's PCG Big Data Platform built digital employees that automate data analysis tasks like weekly reports, reducing delivery from days to minutes through a modular agent architecture combining LLM scheduling, tool protocols, semantic knowledge, memory systems, and evaluation-driven development with three key metrics.

Agent ArchitectureData AnalysisDigital Employee
0 likes · 5 min read
Tencent's Digital Employees Achieve 10x Speedup: Rewritten Agent Architecture Delivers Minutes-Level Data Analysis
Qborfy AI
Qborfy AI
Oct 15, 2026 · Artificial Intelligence

7-Layer Architecture for AI-Native SDLC: How Anthropic, OpenAI, GitHub & Kiro Map to the Stack

This article presents a 7-layer reference architecture for AI-native software development platforms, mapping capabilities from Anthropic, OpenAI, GitHub, and AWS Kiro across infrastructure, specifications, context engineering, agent orchestration, evaluation, production feedback, and governance layers, with a practical migration path from traditional DevOps.

7-layer architectureAI-Native SDLCDevOps migration
0 likes · 19 min read
7-Layer Architecture for AI-Native SDLC: How Anthropic, OpenAI, GitHub & Kiro Map to the Stack
dbaplus Community
dbaplus Community
Oct 14, 2026 · Artificial Intelligence

From Personal AI Coding to Team Agent Workflows: The EP-Harness Platform

This article details how EP-Harness, built on the Multica platform, addresses gaps in personal AI coding by providing a managed agent platform that integrates with team workflows, enabling prompt review, experience reuse, observability, and closed-loop automation for tasks like log inspection and dependency updates.

AI codingAgent workflowAutomated Governance
0 likes · 14 min read
From Personal AI Coding to Team Agent Workflows: The EP-Harness Platform
Qborfy AI
Qborfy AI
Oct 14, 2026 · R&D Management

AI-Native SDLC: The Real Bottlenecks Beyond Code Generation

This article introduces AI-Native SDLC, explaining why code generation is no longer the bottleneck in software development and how the bottleneck has shifted to requirements specification, context management, verification, governance, and production feedback loops, outlining a 7-layer architecture, artifact-driven workflow, and a 9-part series roadmap.

AI-Native SDLCEvent-Driven ArchitectureR&D Process
0 likes · 14 min read
AI-Native SDLC: The Real Bottlenecks Beyond Code Generation
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Oct 14, 2026 · Artificial Intelligence

AI Coding in Legacy Systems: First Build a Queryable Repository Knowledge Layer

The article argues that AI coding in legacy systems fails due to missing cross-module context, proposing a queryable repository knowledge layer that links code structure, business rules, and engineering conventions via graph-structured retrieval, and validates its trade-offs through dependency misuse, defect escape, and graph accuracy metrics.

AI codingSCIPTree-sitter
0 likes · 13 min read
AI Coding in Legacy Systems: First Build a Queryable Repository Knowledge Layer
Ops Community
Ops Community
Oct 13, 2026 · Artificial Intelligence

Context Engineering: Why AI Conversations Drift and How to Prevent It

This article explains why AI models lose accuracy in long conversations due to context degradation, detailing six failure mechanisms and five practical strategies—layered context organization, active compaction, retrieval over accumulation, state externalization, and selective retention—to maintain reliable, cost-effective LLM interactions for operations tasks.

AI ReliabilityKV cacheLLM
0 likes · 67 min read
Context Engineering: Why AI Conversations Drift and How to Prevent It
Top Architecture Tech Stack
Top Architecture Tech Stack
Oct 12, 2026 · Artificial Intelligence

GPT-6 Family Role Split: Match Astra, Sol, Luna to Your Coding Tasks

The article explains how GPT-6's three models — Astra for complex autonomous tasks, Sol for multi-agent coding workflows, and Luna for high-volume structured extraction — should be routed based on task characteristics rather than defaulting to the strongest model, and details reasoning tiers, fast modes, async tool calls, and context engineering practices for cost-effective AI coding agents.

AI agent workflowAI codingGPT-6
0 likes · 11 min read
GPT-6 Family Role Split: Match Astra, Sol, Luna to Your Coding Tasks
Java Tech Enthusiast
Java Tech Enthusiast
Oct 12, 2026 · Artificial Intelligence

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

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

AI engineeringAgent SystemsHarness engineering
0 likes · 24 min read
Harness Engineering: The Third Layer of AI Engineering for Production Systems
Data STUDIO
Data STUDIO
Oct 12, 2026 · Artificial Intelligence

Pi Agent Harness: Minimalist AI Coding Agent Returning Control to Developers

This article analyzes Pi, a minimalist terminal-based AI coding agent by Mario Zechner, detailing its harness architecture, six core components, four integration modes, context engineering via AGENTS.md/SYSTEM.md, tree-shaped sessions, extension system, security considerations, and when to choose it over alternatives like Claude Code or Cursor.

AI coding agentAgent Harness EngineeringCodemode
0 likes · 31 min read
Pi Agent Harness: Minimalist AI Coding Agent Returning Control to Developers
Ops Development & AI Practice
Ops Development & AI Practice
Oct 11, 2026 · R&D Management

Does AI-Generated Documentation Count as Knowledge Accumulation? A Practical Framework

The article evaluates whether using coding agents to write docs, Jira tasks, MR descriptions, and Skills constitutes real knowledge accumulation, proposing four criteria — findable, trustworthy, usable, updatable — and a workflow linking task evidence, ADRs, runbooks, tests, and Skills with continuous validation.

AI-assisted developmentCoding AgentsMCP
0 likes · 24 min read
Does AI-Generated Documentation Count as Knowledge Accumulation? A Practical Framework
Smart Era Software Development
Smart Era Software Development
Oct 11, 2026 · Artificial Intelligence

From AI Coding to AI Working: The 5 Capability Gaps Blocking Agent Deployment

AI coding tools have plateaued, pushing the industry toward AI Working where agents execute full business tasks; the article outlines five critical capability gaps—multi-agent architecture, context and memory engineering, production evaluation, security governance, and business process redesign—and maps them to practitioner-focused conference tracks.

AI AgentsAI governanceagentic engineering
0 likes · 16 min read
From AI Coding to AI Working: The 5 Capability Gaps Blocking Agent Deployment
DataFunSummit
DataFunSummit
Oct 10, 2026 · Artificial Intelligence

Li Auto: Production Agent Bottleneck Is Context Quality, Not Model Capability

Li Auto engineers reveal that production AI Agent failures stem from fragmented context rather than model limits, detailing their ontology-driven four-layer architecture and three architecture iterations that transform context into governable, traceable semantic assets for stable delivery.

AI AgentsKnowledge GovernanceNL2SQL
0 likes · 10 min read
Li Auto: Production Agent Bottleneck Is Context Quality, Not Model Capability
DataFunTalk
DataFunTalk
Oct 7, 2026 · Artificial Intelligence

Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering

The article explains how ontology-driven architecture provides semantic infrastructure for controllable AI Agent execution, detailing three pillars (architectural constraints, context engineering, feedback loops), the Knora platform's layered architecture, and real-world deployments showing 70x efficiency gains.

Agent ControlEnterprise AIHarness engineering
0 likes · 27 min read
Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering
DataFunSummit
DataFunSummit
Oct 6, 2026 · Artificial Intelligence

How 6 Chinese Tech Giants Engineer Context for Production AI Agents

Six leading Chinese companies — JD.com, Hangzhou Qunhe, Guanyuan Data, Datastrato, Li Auto, and AWS — share their production-grade context engineering practices for AI agents, covering layered memory, semantic layers, evidence chains, and action loops at DACon 2026.

AI AgentsDACon 2026Memory Management
0 likes · 22 min read
How 6 Chinese Tech Giants Engineer Context for Production AI Agents
Architect
Architect
Oct 5, 2026 · Artificial Intelligence

AI Agent Architecture: The Runtime Chain Connecting Planner, Tool, MCP, Memory & Reflection

This article deconstructs AI agent architecture as a stateful runtime chain—Goal→Plan→Context→Decide→Act→Observe→Verify→State→Next—distinguishing Planner, Tool, MCP, Memory, and Reflection as interconnected layers rather than parallel modules, emphasizing that reliability comes from the harness runtime handling verification, recovery, and context engineering outside the model.

AI AgentAgent ArchitectureFunction Calling
0 likes · 42 min read
AI Agent Architecture: The Runtime Chain Connecting Planner, Tool, MCP, Memory & Reflection
Linyb Geek Road
Linyb Geek Road
Oct 4, 2026 · Artificial Intelligence

Mastering Claude Code Customization: CLAUDE.md, Rules, Skills, Hooks & Subagents

This article explains the seven customization mechanisms in Claude Code—CLAUDE.md, Rules, Skills, Hooks, Subagents, Output Styles, and append system prompt—detailing their distinct purposes, ideal use cases, and how they collectively enable effective context management for AI-assisted development.

CLAUDE.mdClaude CodeOutput Styles
0 likes · 16 min read
Mastering Claude Code Customization: CLAUDE.md, Rules, Skills, Hooks & Subagents
Architect
Architect
Oct 3, 2026 · Artificial Intelligence

What Counts as a Real Agent Improvement? Lessons from Claude.dev's Four Engineering Articles

Using a subscription billing error as a case study, this article analyzes how to systematically improve AI agents through permission separation, task-specific skill management, reproducible error tracking, rigorous evaluation design, controlled hillclimbing optimization, and deployment validation — drawing on four Claude.dev engineering articles.

AI AgentsClaude Codecontext engineering
0 likes · 22 min read
What Counts as a Real Agent Improvement? Lessons from Claude.dev's Four Engineering Articles
PaperAgent
PaperAgent
Oct 2, 2026 · Artificial Intelligence

Anthropic's Claude Agent Engineering: Multi-Agent Workflows, Skills & Eval

Anthropic publishes its internal Claude agent engineering practices on claude.dev, covering dynamic multi-agent workflows, hundreds of reusable skills, context engineering principles that cut system prompts by 80%, and evaluation-driven hillclimbing that boosted accuracy to 90.5% at one-fifth the cost.

AI AgentsAnthropicClaude Code
0 likes · 13 min read
Anthropic's Claude Agent Engineering: Multi-Agent Workflows, Skills & Eval
DataFunTalk
DataFunTalk
Sep 29, 2026 · Artificial Intelligence

Ontology: The Only Blueprint for Enterprise AI Agents — Forbes & China's Convergence

Forbes Technology Council article argues ontology is the sole blueprint for enterprise AI agents, citing Palantir, Databricks, Microsoft, and Glean convergence; Chinese firms across healthcare, industry, and tech independently hit the same wall, with 30+ case studies at DACon 2026 Beijing demonstrating ontology-driven implementations.

China AI implementationDACon 2026Semantic Layer
0 likes · 19 min read
Ontology: The Only Blueprint for Enterprise AI Agents — Forbes & China's Convergence
DataFunTalk
DataFunTalk
Sep 29, 2026 · Artificial Intelligence

Ontology-Driven Agent Control: From External Guardrails to Internal Semantic Skeletons

The article presents an ontology-driven approach to controllable Agent execution, replacing external prompt-based constraints with internal semantic structures. It details three pillars—architectural constraints, context engineering, and feedback loops—implemented in the Knora platform using a labeled property graph ontology layer, cognitive engine, and Agent execution layer, with a manufacturing case study showing 70x efficiency gains.

AI AgentsAgent ControlHarness engineering
0 likes · 28 min read
Ontology-Driven Agent Control: From External Guardrails to Internal Semantic Skeletons
Architect
Architect
Sep 26, 2026 · Artificial Intelligence

Meta Muse Architecture: Inside the Persistent Personal Agent Execution Environment

This article analyzes Meta's Muse architecture, a persistent personal AI agent execution environment running on dedicated cloud VMs, detailing its Runtime Cell isolation, Sentinel authorization control plane, credential proxy injection, tainted egress protection, browser isolation, and multi-layer state management for long-running tasks, while comparing with Codex, OpenClaw, Hermes, and DSH approaches.

AI Agent ArchitectureCredential ProxyLong-running Tasks
0 likes · 36 min read
Meta Muse Architecture: Inside the Persistent Personal Agent Execution Environment
DataFunSummit
DataFunSummit
Sep 26, 2026 · Artificial Intelligence

Ontology-Driven Agent Control: The Semantic Backbone for Harness Engineering

This article explores how ontology-driven architecture solves the uncontrollability of LLM agents in enterprise settings by replacing external prompt-based constraints with explicit semantic modeling, detailing Knora's three-layer system that enables deterministic verification, precise context retrieval, and continuous ontology evolution from execution feedback, with real-world 70x efficiency gains in railway reporting.

Agent ControlEnterprise AIHarness engineering
0 likes · 27 min read
Ontology-Driven Agent Control: The Semantic Backbone for Harness Engineering
Architect
Architect
Sep 25, 2026 · Artificial Intelligence

From LLM Call to Full Harness: Understanding Agent Runtime Layers

The article traces the evolution from a simple LLM call to a complete Harness for AI agents, using a timeout-fixing task to illustrate the layered boundaries between model, loop, and harness, covering context engineering, tool execution, state management, verification loops, and design trade-offs in systems like Pi, Cursor, and Codex.

LLM RuntimeState ManagementTool Calling
0 likes · 34 min read
From LLM Call to Full Harness: Understanding Agent Runtime Layers
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 25, 2026 · Artificial Intelligence

From Prompt Engineering to Graph Engineering: The Evolution of AI App Development Paradigms

The article outlines five engineering disciplines—Prompt, Context, Harness, Loop, and Graph Engineering—that progressively address the challenges of building reliable LLM applications, from crafting effective prompts to managing complex task execution graphs with state, tools, and human-in-the-loop controls.

AI AgentGraph EngineeringHarness engineering
0 likes · 17 min read
From Prompt Engineering to Graph Engineering: The Evolution of AI App Development Paradigms
DataFunTalk
DataFunTalk
Sep 25, 2026 · Artificial Intelligence

Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering

This article explores how ontology-driven architecture provides a semantic foundation for controllable AI agents, detailing the Knora platform's three-layer design that replaces external prompt-based constraints with internalized business rules, enabling precise context retrieval, verifiable feedback loops, and measurable efficiency gains in industrial deployments.

AI AgentsEnterprise AIFeedback Loops
0 likes · 26 min read
Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering
JD Tech
JD Tech
Sep 24, 2026 · Artificial Intelligence

JD Haibo's AI Knowledge Base: 3-Layer Architecture, OKF Spec & 44% Faster AB Test

JD Haibo's team built a structured AI knowledge base using OKF specification, a three-layer architecture (project-domain matrix, role-based views, skills), and a closed-loop lifecycle with automated generation and human validation; AB experiments showed 44% faster requirement analysis and more comprehensive coverage of cross-system dependencies.

AB TestingAI knowledge baseJD Haibo
0 likes · 37 min read
JD Haibo's AI Knowledge Base: 3-Layer Architecture, OKF Spec & 44% Faster AB Test
TonyBai
TonyBai
Sep 24, 2026 · Artificial Intelligence

Jev Engineering: Rethinking Coding Agent Architecture Beyond KV Cache

The article analyzes six systemic flaws in current coding agents caused by KV cache dependency, proposing a Harness framework centered on the Jev decision model that dynamically assembles context, routes models with full cost accounting, progressively discloses tools, binds instructions to context triggers, and shares read-only retrieval across background pipelines.

AGENTS.mdCoding AgentsHarness framework
0 likes · 38 min read
Jev Engineering: Rethinking Coding Agent Architecture Beyond KV Cache
Architect
Architect
Sep 23, 2026 · Artificial Intelligence

Supervisor Agent Pattern: Dynamic Task Routing, Bottleneck Risks, and Control Patterns

This article analyzes the Supervisor pattern in multi-agent systems, explaining how central agents dynamically route tasks based on intermediate results, comparing 'agents as tools' vs. handoff control patterns, detailing context engineering practices, termination conditions, logging requirements, and why Supervisor architectures can become information bottlenecks despite their flexibility.

AI Agent ArchitectureMulti-Agent Systemsagent orchestration
0 likes · 23 min read
Supervisor Agent Pattern: Dynamic Task Routing, Bottleneck Risks, and Control Patterns
JD Tech Talk
JD Tech Talk
Sep 23, 2026 · R&D Management

JD Haibo's AI-Native Knowledge Base: 3-Layer Architecture, OKF Spec & 44% Faster Dev

JD Haibo built a three-layer AI knowledge base (project-domain matrix, role-specific views, executable skills) using the OKF specification, auto-generating structured docs from code, integrating with Harness for context-aware AI assistance, and validating via AB tests showing 44% faster requirement analysis with better coverage.

AI-nativeHarness²Knowledge Base
0 likes · 35 min read
JD Haibo's AI-Native Knowledge Base: 3-Layer Architecture, OKF Spec & 44% Faster Dev
Smart Era Software Development
Smart Era Software Development
Sep 20, 2026 · Artificial Intelligence

Why 80% of Agent Decisions Fail: Deep Dive into ADPS Perception Patterns P1–P4

The article reveals that most agent failures stem from perception flaws, not model limits, and details ADPS's four-layer perception funnel, four ingestion modes, four core design patterns (Context Triage, Semantic Compaction, Progressive Discovery, Multi-Modal Fusion), three anti-patterns, and a security warning — all grounded in production case studies from Tencent, Weibo, and game teams.

ADPSAI AgentsAgent Design Patterns
0 likes · 35 min read
Why 80% of Agent Decisions Fail: Deep Dive into ADPS Perception Patterns P1–P4
AI Large Model Application Practice
AI Large Model Application Practice
Sep 20, 2026 · R&D Management

AI Coding 5x Faster, Delivery Only 10% Quicker: The Enterprise Engineering Time Lag

The article analyzes why faster AI coding doesn't proportionally accelerate enterprise software delivery, identifying bottlenecks in requirements, context engineering, verification, and CI/CD, and proposes layered testing, knowledge management, and human-in-the-loop processes to align the entire pipeline with AI speed.

AI-assisted developmentCI/CD pipelinecontext engineering
0 likes · 20 min read
AI Coding 5x Faster, Delivery Only 10% Quicker: The Enterprise Engineering Time Lag
Architect
Architect
Sep 16, 2026 · Artificial Intelligence

Google Agentic Skills: Survey, Engineering Practices & Lifecycle Framework

This article reviews Google's Agentic Skills survey and engineering practices, covering a formal six-tuple skill definition, three criteria for true skills, a nine-stage lifecycle, security governance, skill debt, conditional benefits, and complementary research on WikiSkill and SKILL.state, concluding with six critical questions for production skill deployment.

Agent ArchitectureAgentic SkillsGoogle
0 likes · 22 min read
Google Agentic Skills: Survey, Engineering Practices & Lifecycle Framework
JD Tech
JD Tech
Sep 11, 2026 · Product Management

How a B2B PM Cut Requirement Work from 34 to 15 Person-Days with AI Workflows

A B2B product manager shares a practical AI workflow — PRD four-step method, Skill reuse, and Context/Harness engineering — that reduced requirement work from 34 to 15 person-days while maintaining quality, with detailed case studies, boundaries, and learning strategies.

AI workflowB2B Product ManagementHarness engineering
0 likes · 49 min read
How a B2B PM Cut Requirement Work from 34 to 15 Person-Days with AI Workflows
Subtle Storm
Subtle Storm
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
Geek Labs
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
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 AIEnterprise Architecture
0 likes · 27 min read
Enterprise AI's Real Moat: How Glean, Palantir, and OpenAI Build Context
DeepHub IMBA
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 workflowFeedback Loops
0 likes · 15 min read
Prompt Engineering vs Loop Engineering: Hierarchy, Automation, and When to Use Each
ThinkingAgent
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
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 AgentsBenchmarkingUber
0 likes · 45 min read
Uber's AI Software Factory: How Enterprises Turn Coding Agents into Measured Production
AndroidPub
AndroidPub
Aug 28, 2026 · R&D Management

From Docs to Decisions: Redefining Requirements in AI-Assisted Development

This article traces a multi-year evolution from using early LLMs to generate flowcharts from chat logs, through piloting Cursor and Markdown for requirements, to a 'zero-day delivery' method where a running system anchors scope definition, concluding that AI shifts the bottleneck upstream: requirements definition becomes about deciding what to build, not documenting it.

AI codingCursorLLM
0 likes · 19 min read
From Docs to Decisions: Redefining Requirements in AI-Assisted Development
Fighter's World
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
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
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 ArchitectureCodex HarnessJSON-RPC
0 likes · 33 min read
Codex Harness: How OpenAI Built an Embeddable Agent OS in 135 Rust Crates
The Dominant Programmer
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
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 codingSoftware Engineering
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
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 LoadingState Management
0 likes · 12 min read
Mastering AI Context Engineering: The Four Core Components Explained
Thought Artisan
Thought Artisan
Aug 15, 2026 · Artificial Intelligence

Six Core Principles for Building Reliable AI Agent Systems

This article outlines six key principle categories for designing effective AI agent systems: simplicity, transparency, tool interface design, harness constraints, context engineering, evaluation, and multi-agent collaboration, emphasizing iterative evolution and cost-aware decisions.

AI AgentsAgent Design PrinciplesMulti-Agent Systems
0 likes · 9 min read
Six Core Principles for Building Reliable AI Agent Systems
Chengwu Tech Stack
Chengwu Tech Stack
Aug 15, 2026 · Artificial Intelligence

Why AI Agents Fail in Enterprises: The Case for Organizational Context Engineering

This article explains why AI agents that excel in personal projects falter in enterprise environments, arguing that the gap stems not from model limitations but from fragmented organizational knowledge, and proposes a five-layer context engineering framework to make companies AI-readable through versioned context packages, permission controls, and evidence-driven feedback loops.

AI AgentsAgent ArchitectureEnterprise AI
0 likes · 28 min read
Why AI Agents Fail in Enterprises: The Case for Organizational Context Engineering
DataFunSummit
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 AgentsAgent ControlHarness engineering
0 likes · 20 min read
How Ontology‑Driven Harness Engineering Enables Controllable Agent Execution
Java Tech Enthusiast
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
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
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
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 MemoryContinual LearningLLM
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
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 Testingcontext engineering
0 likes · 19 min read
From AI Gibberish to Precise Code Changes: How I Made AI Understand a Legacy Project
TechVision Expert Circle
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 programmingcode generationcontext engineering
0 likes · 15 min read
Designing an AI Auto‑Programming System That Outpaces Junior Developers
Machine Heart
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.

AIAgentsStartup Strategy
0 likes · 26 min read
From TPU to Self‑Evolving Agents: Jeff Dean’s Take on AI’s Next Step
AI Architecture Hub
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
DataFunSummit
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 AgentsKnoracontext engineering
0 likes · 20 min read
Harness Engineering’s Semantic Foundation: Ontology‑Driven, Controllable Agents
PaperAgent
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
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
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 developmentAnthropicBest Practices
0 likes · 11 min read
Why Anthropic Cut 80% of Claude Code System Prompts Overnight
Tech Ocean
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 modelBest PracticesClaude
0 likes · 13 min read
Why Cutting 80% of Claude’s System Prompt Improves Performance and How to Slim Your CLAUDE.md
Machine Heart
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
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
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
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 Engineer Programming
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
13 Agentic AI Trends to Watch in 2026
DataFunTalk
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
Agent Harness Unpacked: A Deep Dive into AI Agent Architecture
Linyb Geek Road
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
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
DataFunTalk
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
Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agents
Data Party THU
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
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
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‑improvementEvolutionary SearchHarness engineering
0 likes · 17 min read
How AI Can Achieve Recursive Self‑Improvement: Lilian Weng Says Build a Robust Harness First
ThinkingAgent
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
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.

AgentHarness²LLM
0 likes · 17 min read
20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)
DataFunSummit
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
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
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 AgentsControllable AIKnora
0 likes · 19 min read
Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agent Execution
AI Engineer Programming
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
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 AgentsAgent workflowPrompt design
0 likes · 15 min read
How Skill Packages Turn AI Agents into Scenario Experts
DataFunSummit
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)
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
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.

LLMToken Optimizationagent harness
0 likes · 27 min read
Productionizing LLM Agent Harness: Architecture, Backend Design, and Optimization
ThinkingAgent
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
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
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
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 AgentsHarness engineeringLoop engineering
0 likes · 16 min read
8 Crucial Questions to Understand Loop Engineering and the New Agent Paradigm