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Advanced Ontology Design: From a Reasonable Model to a Production-Grade Semantic Layer for Agents
When an ontology has to go to production: pick the right OWL profile, turn constraints into executable checks, handle identity and time, and let agents use it through a tool contract. This is about making it hold up, not making it look pretty.
Ontology Design from Scratch: Building a Reasoning-Ready Business Semantics Layer
Same model, same tools, same data — yet the agent is sometimes right and sometimes wrong. The difference is rarely the model; it is whether there is a shared, computable, constrainable layer of business semantics. This guide starts from zero and ends with a working minimal accounting ontology.
When AI Learns to Pentest: The ARTEX Case and the Rise of Agentic Attack Tooling
In October 2026, an open-source AI pentesting tool called ARTEX was linked to attacks on South Korean financial institutions — and within 48 hours its developer pulled the source and went closed-source. This article reconstructs the timeline, unpacks its technical positioning, and answers the more important question: how should defenders respond when attack tooling is driven by AI agents?
Honeypots and Anti-Agent Traps: When the Prey Starts Setting Traps
Honeypots are a classic defensive tool, but once attackers became AI agents both sides changed: defenders now design traps aimed specifically at AI, and anyone using agents to read external content needs to know how to avoid them. This article unpacks four trap classes and how to defend against them.
Multi-Agent Coordination: How Planners and Workers Avoid Fighting Each Other
Hand a task to several AI agents in parallel and three problems appear fast: duplicated work, information silos, and dependency mis-ordering. This article unpacks four design techniques from a public agentic system — role red lines, process-level information exchange, a shared todo list, and a single intent generator — and how they map onto any multi-agent workflow.
Before You Start: You Only Need to Know These 20 Terms
You can get started with AI Agents without writing code — as long as you understand what the docs are saying. This article splits the 20 terms that actually block beginners into four groups, gives a one-sentence definition for each plus which to read first, and explains how to use the glossary and in what order to finish a first pass.
What Is an AI Agent: From ChatGPT to AI That Acts on Its Own
Break down the essence of AI Agents with four components — LLM, context, tools, and loop — contrast chatbots with Agents, walk through daily, developer, and business examples plus real failure modes, and learn to judge whether your needs actually call for an Agent.
Infrastructure-izing Context Compression: How Headroom Turns Token Cost from a Tactical Problem into System Architecture
Headroom 25.8K⭐ · A new species of Agent infrastructure, not a cost-saving tool but an infrastructure layer that makes long-running AI Agents economically viable. A 6-layer compression pipeline + the reversible CCR design + a 16x academic breakthrough validating that the route is correct.
Agent-Reach: Giving AI Agents a Pair of Eyes Over the Entire Internet
GitHub Trending #7 · 5.2K ⭐/week · Lets AI Agents search and read content from 10+ platforms including Twitter/Reddit/YouTube/Bilibili/Xiaohongshu, with no API fees, using pure web scraping
Agent Evolver Deep Dive: The Evolution Engine That Lets AI Agents Grow Like Humans
After prolonged use of an Agent, its core files (SOUL, AGENTS, USER, MEMORY, RULES) keep expanding, and old rules conflict with new directions. Agent Evolver introduces a human growth model: periodic self-reflection, identifying outdated beliefs, and reshaping itself under user approval. This article breaks down its philosophical foundations, three-dimensional evaluation system, growth trigger mechanism, and safety design, and explains why it is the most underrated "evolution layer" in Agent infrastructure.
Pre-mortem: Why AI Agents Need to Imagine Their Own Failure Before They Start
The application of the Pre-mortem methodology in AI Agent systems. Explore how Agent Previsor uses multi-scenario divergent path forecasting to move the regret of "I should not have done it that way" from after execution to before execution, based on chess move calculation, military war-gaming, and a complete analysis across four forecasting dimensions.
Agentic Infrastructure: A Seven-Layer Architecture Defining How AI Agents Should Exist
From "skills that cannot be triggered" to "how an Agent should evolve itself": seven open-source skills, seven layers of architecture, one complete system of Agent self-awareness. Based on a field audit of 125 skills and 6 months of iteration through pitfalls.
Skill Reporting: Breaking the AI Agent Black Box with One Line of Text - The Design Philosophy and Practice of Institutional Skills
Agent replied to you, but you have no idea which skills it used, what process it followed, or where the data came from. Every reply feels like a black box. Transparency is the third-largest barrier to enterprise adoption of AI Agents (Deloitte 2026). Skill Reporting breaks the black box with one line of text: no code required, just add one permanent rule to RULES.md. This article deeply analyzes the design philosophy, real-world effects, and indirect impacts of institutional skills.
Vector Memory Deep Dive: A Production-Grade Memory System That Ensures AI Agents Never Lose Memory Again
State amnesia is the #1 killer in production Agent environments. We built a four-layer vector memory system based on Qdrant + BGE-m3, with 9 retrieval modes, validated with 6,675 memories in real-world use, raising Chinese search accuracy from 0% to >78%. One line curl | bash, 100% local deployment, and data sovereignty stays in your hands.
From Zero to Launch: Agentics Website Development Retrospective and Key Lessons
A complete development retrospective on building a Next.js knowledge platform from scratch with AI Agents (our AI assistant + Claude Code): architecture design, pitfalls, key decisions, and best practices for AI-assisted development.