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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 DesignContext Engineering

0 likes · 13 min read

2026 AI Product Manager: The Essential Capability Model

AI Tech Publishing

Apr 22, 2026 · Artificial Intelligence

Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

The article analyzes how extending the context window of large language models leads to rapid forgetting, outlines seven concrete failure modes, examines cognitive‑science‑based memory architectures, and walks through practical layers—from Python lists to markdown files to vector retrieval—highlighting why simple context expansion alone cannot solve the problem.

Agent DesignContext WindowLLM Memory

0 likes · 10 min read

Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

Wu Shixiong's Large Model Academy

Apr 21, 2026 · Artificial Intelligence

When Should an LLM Agent Extract Memory? A Deep Dive into Trigger Strategies

The article analyzes why memory extraction in LLM‑driven agents incurs cost, compares four frameworks—Claude Code, Generative Agents, MemGPT, and Mem0—detailing their trigger mechanisms, concurrency handling, and trade‑offs, and offers practical guidance for choosing the right strategy in real‑time, social, or batch‑processing scenarios.

AI EngineeringAgent DesignLLM

0 likes · 18 min read

When Should an LLM Agent Extract Memory? A Deep Dive into Trigger Strategies

AntData

Apr 17, 2026 · Industry Insights

5 Silver Rules That Made Dataphin‑MCP’s AI Platform Scale to 1M Calls in 9 Days

This article shares the practical lessons learned from building Dataphin‑MCP, an AI‑enabled data‑development platform, by outlining five concrete "silver" rules, illustrating each with real‑world cases, and discussing deeper considerations for building robust AI‑first tools and harnesses.

AI PlatformAgent DesignConcept modeling

0 likes · 13 min read

5 Silver Rules That Made Dataphin‑MCP’s AI Platform Scale to 1M Calls in 9 Days

Sohu Tech Products

Apr 15, 2026 · Artificial Intelligence

Why Harness Engineering Is the Next Evolution in AI System Design

This tutorial explains the three-stage evolution from Prompt Engineering to Context Engineering and finally Harness Engineering, detailing their motivations, core components, practical implementations, and why stable, end‑to‑end AI agents require a full harness to manage tasks, context, tools, execution, state, and error recovery.

AI systemsAgent DesignContext Engineering

0 likes · 31 min read

Why Harness Engineering Is the Next Evolution in AI System Design

JavaGuide

Apr 14, 2026 · Artificial Intelligence

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

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

Agent DesignContext EngineeringLLM Security

0 likes · 23 min read

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

AI Tech Publishing

Apr 14, 2026 · Artificial Intelligence

12 Harness Design Patterns from Claude Code: Memory, Workflow, Tools, and Automation

The article dissects twelve concrete harness design patterns uncovered in the leaked Claude Code source, organized into four categories—memory & context, workflow & orchestration, tools & permissions, and automation—detailing their use cases, trade‑offs, and implementation costs for building production‑grade AI agents.

Agent DesignAutomationClaude Code

0 likes · 14 min read

12 Harness Design Patterns from Claude Code: Memory, Workflow, Tools, and Automation

AI Step-by-Step

Apr 1, 2026 · Artificial Intelligence

When to Use Which Model in an Agent: Beyond the “Strongest Model” Myth

The article explains why routing every request to the most powerful LLM hurts cost, speed, and throughput, and presents a three‑layer task decomposition that assigns execution‑level tasks to cheap small models, intermediate tasks to mid‑size models, and high‑risk judgment tasks to large models, with concrete examples and a minimal routing strategy.

Agent DesignCost OptimizationLLM

0 likes · 8 min read

When to Use Which Model in an Agent: Beyond the “Strongest Model” Myth

Qborfy AI

Mar 31, 2026 · Artificial Intelligence

Mastering AI Agents with the Plan-and-Solve Design Pattern

The article introduces the Plan-and-Solve design pattern for AI agents, explaining how separating planning and execution improves handling of complex tasks, compares it with ReAct, provides detailed workflow diagrams, concrete examples such as weekly report generation, and offers a full Python implementation with dynamic replanning and result aggregation.

AI agentsAgent DesignLLM

0 likes · 14 min read

Mastering AI Agents with the Plan-and-Solve Design Pattern

Data Party THU

Mar 30, 2026 · Artificial Intelligence

Why AI Needs a ‘Harness’: Building Environments for Persistent Agents

The article analyzes the emerging concept of Harness Engineering—combining AI models with structured environments, standards, and feedback loops—to enable agents that can work continuously, illustrated by OpenAI and Anthropic case studies, practical design guidelines, and a three‑week adoption plan.

AI EngineeringAgent DesignHarness Engineering

0 likes · 10 min read

Why AI Needs a ‘Harness’: Building Environments for Persistent Agents

Design Hub

Mar 27, 2026 · Artificial Intelligence

What Problem Does Claude Code’s Auto Mode Actually Solve?

Anthropic’s new Auto Mode for Claude Code inserts a middle ground between manual approvals and unrestricted execution by letting the model approve low‑risk actions while blocking potentially dangerous ones, using a two‑stage classifier that evaluates intent and real‑world impact with concrete safety metrics.

AI SafetyAgent DesignClaude Code

0 likes · 12 min read

What Problem Does Claude Code’s Auto Mode Actually Solve?

Java Tech Enthusiast

Mar 15, 2026 · Artificial Intelligence

Why OpenClaw’s Uninstall Storm Exposes Critical AI Agent Security Flaws

A sudden wave of OpenClaw uninstall services in 2026 revealed severe AI agent security risks, including default open‑network configurations, persistent OAuth tokens, malicious plugins, runaway costs, and stability crashes, prompting a deep analysis of design flaws and recommended safeguards for future intelligent agents.

AI SafetyAI agentsAgent Design

0 likes · 10 min read

Why OpenClaw’s Uninstall Storm Exposes Critical AI Agent Security Flaws

AI Tech Publishing

Mar 2, 2026 · Artificial Intelligence

Why pi-mono’s Agent Design Is an Anti‑Pattern (and What Works Better)

The author explains why Claude Code became too bloated, outlines the minimal, controllable requirements for a code‑assistant, details pi-mono’s four‑package architecture, shares design anti‑patterns, and presents benchmark results showing its simple approach outperforms heavier agents.

Agent DesignBenchmarkClaude Opus

0 likes · 13 min read

Why pi-mono’s Agent Design Is an Anti‑Pattern (and What Works Better)

Architecture Digest

Feb 28, 2026 · Artificial Intelligence

Why AI Can’t Replace Engineers: The Rise of the Post‑Processing Engineer

The article explains how large‑model AI can quickly generate seemingly functional code but still lacks product logic, boundary awareness, and security, forcing engineers to act as “post‑processing engineers” who proofread, refactor, and polish AI‑generated artifacts into reliable, production‑ready software.

Agent DesignCode GenerationSoftware Engineering

0 likes · 8 min read

Why AI Can’t Replace Engineers: The Rise of the Post‑Processing Engineer

Architecture and Beyond

Feb 8, 2026 · Artificial Intelligence

Designing Scalable Long-Term Memory for AI Agents: Capture, Compress, Retrieve

This article explains how to build a controllable, editable, and cost‑effective long‑term memory system for AI agents by categorizing memory types, structuring a three‑stage pipeline of capture, AI‑driven compression, and smart retrieval, and choosing appropriate storage back‑ends such as files, knowledge bases, or databases.

Agent DesignKnowledge BaseLong-term Memory

0 likes · 18 min read

Designing Scalable Long-Term Memory for AI Agents: Capture, Compress, Retrieve

Frontend AI Walk

Jan 29, 2026 · Artificial Intelligence

AI Programming Concepts: Rules, Commands, Subagents, MCP, Skills, Modes, Hooks

The article systematically defines and compares the seven key AI programming abstractions—Rules, Commands, Subagents, MCP, Skills, Modes, and Hooks—detailing their core characteristics, typical use cases, implementation patterns, and best‑practice guidelines while highlighting common misunderstand‑ings and design trade‑offs.

AI programmingAgent DesignModes

0 likes · 18 min read

AI Programming Concepts: Rules, Commands, Subagents, MCP, Skills, Modes, Hooks

Programmer's Advance

Jan 25, 2026 · Artificial Intelligence

Can 20‑30 AI Agents Outpace a Single Assistant? Inside Gas Town

Gas Town, a multi‑agent orchestration system introduced by former Google engineer Steve Yegge, coordinates 20‑30 Claude Code instances using Git‑backed hooks, beads, and a Mayor coordinator, promising parallel development, versioned state, fault tolerance, and sparking industry debate on the future of AI‑driven software engineering.

AI agentsAgent DesignGit persistence

0 likes · 24 min read

Can 20‑30 AI Agents Outpace a Single Assistant? Inside Gas Town

AI Engineering

Jan 22, 2026 · Artificial Intelligence

MCP Isn’t Broken – It’s Your Server Design, Not the Protocol

The article argues that the recent hype around Skills and the criticism of MCP stem from a mindset problem: developers treat MCP like a REST API wrapper, leading to poor server designs, and it offers six concrete design principles to build agent‑friendly MCP services.

AI toolingAgent DesignMCP

0 likes · 11 min read

MCP Isn’t Broken – It’s Your Server Design, Not the Protocol

Architecture and Beyond

Dec 21, 2025 · Artificial Intelligence

Designing RAG for Industry‑Specific AI Agents: From Data to Safe Execution

This article explains how to build Retrieval‑Augmented Generation (RAG) for industry‑specific AI agents, covering required capabilities, metrics, data sources, indexing, hybrid retrieval, decision‑point integration, layered output, permission controls, rollout strategies, and common pitfalls to ensure reliable and secure automation.

Agent DesignKnowledge RetrievalRAG

0 likes · 17 min read

Designing RAG for Industry‑Specific AI Agents: From Data to Safe Execution

Alibaba Cloud Developer

Oct 30, 2025 · Artificial Intelligence

Why AI Agents Aren’t As Simple As They Appear: Engineering Challenges and Solutions

Building AI agents may seem straightforward with frameworks like LangChain, but hidden complexities in orchestration, memory management, reproducibility, and scalability turn simple demos into fragile systems, requiring systematic engineering, observability, and robust design to achieve reliable, production‑grade intelligent agents.

AI agentsAgent DesignLangChain

0 likes · 21 min read

Why AI Agents Aren’t As Simple As They Appear: Engineering Challenges and Solutions

Instant Consumer Technology Team

Oct 17, 2025 · Artificial Intelligence

Mastering Context Engineering for AI Agents: Overcome Overload with Smart Strategies

This article distills Anthropic’s “Effective Context Engineering for AI Agents” into key insights, explaining why context engineering matters, how it differs from prompt engineering, what constitutes good practice, and practical techniques—system prompts, tool design, few‑shot prompting, compaction, structured note‑taking, and sub‑agent architectures—to mitigate context overload in large language model agents.

AI agentsAgent DesignContext Engineering

0 likes · 10 min read

Mastering Context Engineering for AI Agents: Overcome Overload with Smart Strategies

Architecture and Beyond

Sep 6, 2025 · Artificial Intelligence

How AI Agents Manage Context: Compression Strategies from Manus, Claude Code, and Gemini CLI

This article examines the context explosion problem in AI agents and compares three distinct compression approaches—Manus's never‑lose philosophy, Claude Code's aggressive 92% threshold with eight‑section summaries, and Gemini CLI's balanced 70% trigger with curated history—highlighting their trade‑offs in performance, cost, and reliability.

AIAgent DesignLLM

0 likes · 19 min read

How AI Agents Manage Context: Compression Strategies from Manus, Claude Code, and Gemini CLI

Alibaba Cloud Developer

Jul 21, 2025 · Artificial Intelligence

Unlocking LLM Power: How Context Engineering Transforms AI Assistants

Context engineering, the emerging discipline of structuring and managing input information for large language models, goes beyond simple prompt design by addressing issues such as context poisoning, overload, and conflict, offering strategies like intelligent retrieval, isolation, pruning, and compression to build reliable, high‑performing AI agents.

AI productivityAgent DesignContext Engineering

0 likes · 19 min read

Unlocking LLM Power: How Context Engineering Transforms AI Assistants

DataFunSummit

Jun 8, 2025 · Artificial Intelligence

Mastering LLM Applications: Practical Agent Design and Implementation Strategies

This comprehensive guide explores the core implementation paths for large language model (LLM) applications, focusing on agent design, workflow orchestration, tool integration, memory management, multi‑agent architectures, and future trends, providing actionable methodologies and real‑world examples for practitioners.

AI AgentAgent DesignLLM

0 likes · 25 min read

Mastering LLM Applications: Practical Agent Design and Implementation Strategies

Tencent Technical Engineering

Apr 25, 2025 · Artificial Intelligence

Practical Guide to Building Effective AI Agents and Workflows

Fred’s practical guide expands Anthropic’s “Build effective agents” by offering a technical selection framework, clear definitions of agents versus workflows, a suite of reusable design patterns such as prompt‑chain routing and orchestrator‑worker loops, real‑world case studies, and concrete implementation tips that emphasize simplicity, transparency, and effective tool‑prompt engineering.

AI agentsAgent DesignLLM workflows

0 likes · 25 min read

Practical Guide to Building Effective AI Agents and Workflows

AI Frontier Lectures

Mar 6, 2025 · Artificial Intelligence

Can General AI Agents Evolve from Data Gatherers to Professional Deliverables?

The article evaluates the Manus agent’s current strengths in information‑gathering tasks, contrasts collaborative versus fully‑delegated agent models, identifies structural and context limitations that hinder professional‑grade outputs, and speculates on how future agents might bridge this gap.

AIAgent DesignCollaborative AI

0 likes · 5 min read

Can General AI Agents Evolve from Data Gatherers to Professional Deliverables?

Architect

Dec 30, 2023 · Big Data

Designing a Scalable Log Collection Agent: Lessons from Vivo’s Bees‑Agent

This article details the end‑to‑end design of Vivo’s custom log‑collection agent, covering file discovery with inotify, unique file identification using inode and content hash, real‑time reading via RandomAccessFile, checkpointing, Kafka integration, offline HDFS ingestion, resource throttling, and platform‑wide management, while comparing it with open‑source alternatives.

Agent DesignBig DataKafka

0 likes · 26 min read

Designing a Scalable Log Collection Agent: Lessons from Vivo’s Bees‑Agent

Architecture Digest

Dec 2, 2022 · Big Data

Design and Implementation of Vivo's Bees Log Collection Agent

This article presents the design principles, core techniques, and practical solutions of Vivo's self‑developed Bees log collection agent, covering file discovery, unique identification, real‑time and offline ingestion, checkpointing, resource control, platform management, and a comparison with open‑source alternatives.

Agent DesignJavaKafka

0 likes · 25 min read

Design and Implementation of Vivo's Bees Log Collection Agent

vivo Internet Technology

Nov 23, 2022 · Big Data

Design and Implementation of Vivo's Bees Log Collection Agent

Vivo’s Bees‑agent is a custom, lightweight log‑collection service that discovers rotating files via inotify, uniquely identifies them with inode and hash signatures, supports real‑time and offline ingestion to Kafka and HDFS, offers checkpoint‑resume, resource isolation, rich metrics, and a centralized management platform, outperforming open‑source collectors in latency, memory usage, and scalability.

Agent DesignHDFSJava

0 likes · 24 min read

Architect

Dec 23, 2020 · Operations

Design and Evaluation of Log Collection Agents: Flume vs Filebeat

This article analyses the shortcomings of traditional log‑collection agents, compares Flume and Filebeat based on low‑cost, stability, efficiency and lightweight criteria, and presents practical solutions for file discovery, offset tracking, multi‑line handling and performance tuning in modern logging pipelines.

Agent DesignFlumeObservability

0 likes · 13 min read

Design and Evaluation of Log Collection Agents: Flume vs Filebeat

Top Architect

Jun 28, 2020 · Operations

Design and Implementation of a Log Collection Agent: Challenges and Solutions

This article explains the evolution of logging, the role of log‑collection agents, industry solutions, and step‑by‑step techniques for building a reliable push‑mode log collector on Linux, covering file discovery, offset management, file identification, update detection, and safe resource release.

Agent DesignFile MonitoringLinux

0 likes · 17 min read

Design and Implementation of a Log Collection Agent: Challenges and Solutions