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memory system

19 articles · Page 1 of 1
AI Large Model Application Practice
AI Large Model Application Practice
Jul 13, 2026 · Artificial Intelligence

20 Essential Agent Engineering Concepts for 2026: Making Agents Practical, Scalable, and Deployable

The article breaks down ten core engineering pillars for production‑grade AI agents—including tool calling with MCP, reusable skills, persistent memory, multi‑agent collaboration, workflow orchestration, hooks, observability, sandboxing, prompt‑injection defense, and the role of forward‑deployed engineers—to help turn demo agents into reliable, enterprise‑ready systems.

Agent EngineeringMulti-AgentObservability
0 likes · 21 min read
20 Essential Agent Engineering Concepts for 2026: Making Agents Practical, Scalable, and Deployable
Tencent Cloud Developer
Tencent Cloud Developer
Jul 3, 2026 · Artificial Intelligence

Deep Architectural Review of WorkBuddy: The New Paradigm for AI Office Agents

WorkBuddy, launched by Tencent Cloud in March 2026, is a zero‑setup AI agent that turns chat into execution by offering three operation modes, a three‑layer memory system, multi‑model switching, a skill marketplace, multi‑agent collaboration, automated scheduling and a secure sandbox, and its performance is evaluated across code development, stock analysis and content creation scenarios, highlighting both strengths and current limitations.

AI AgentAutomationSecurity
0 likes · 13 min read
Deep Architectural Review of WorkBuddy: The New Paradigm for AI Office Agents
Architect's Ambition
Architect's Ambition
May 8, 2026 · Artificial Intelligence

A 12,000‑Word Guide to Agent Harness: Designing and Implementing Production‑Ready AI Agents

The article presents a comprehensive 7‑layer Agent Harness architecture that transforms experimental LLM‑based agents into stable, cost‑effective, secure, and observable production‑grade autonomous workers, illustrated with real‑world case studies, performance metrics, and concrete implementation details.

AI AgentsAgent ArchitectureMulti-agent collaboration
0 likes · 33 min read
A 12,000‑Word Guide to Agent Harness: Designing and Implementing Production‑Ready AI Agents
Tinker Programmer
Tinker Programmer
May 3, 2026 · Artificial Intelligence

Why 99% of AI Agents Fail and How to Avoid Common Pitfalls

Most developers mistake model capability for system capability, leading to unstable agents; this article breaks down six essential modules—four‑layer architecture, execution model, memory system, framework choice, multi‑agent design, and observability—to guide engineers toward production‑ready AI agents.

AI AgentsObservabilityexecution model
0 likes · 6 min read
Why 99% of AI Agents Fail and How to Avoid Common Pitfalls
Java Backend Technology
Java Backend Technology
Apr 26, 2026 · Artificial Intelligence

Why Claude Code Says Nothing Unnecessary: Inside Its Minimalist Prompt Design

The article dissects Claude Code’s lean output by exposing the meticulously crafted system prompts that enforce a strict engineering‑assistant role, safety boundaries, concise output rules, and disciplined Git workflows, revealing how each rule curtails hallucination and over‑engineering while enabling reliable, task‑focused code generation.

AI code assistantClaude CodeGit workflow
0 likes · 9 min read
Why Claude Code Says Nothing Unnecessary: Inside Its Minimalist Prompt Design
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 17, 2026 · Backend Development

How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction

This article dissects Claude Code’s Memory subsystem, explaining the distinction between Session logs and persistent Memory, the SHA‑256‑based storage layout, file indexing, four memory types, prompt injection steps, two write pathways, the ExtractionCoordinator’s coalescing strategy, and how to explain the design in interviews.

Claude CodePrompt Engineeringbackend architecture
0 likes · 19 min read
How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction
Linyb Geek Road
Linyb Geek Road
Apr 15, 2026 · Artificial Intelligence

Designing a Stateful Multi‑Turn Dialogue Agent on Stateless LLMs

Building a production‑grade multi‑turn dialogue agent requires managing LLM’s statelessness by combining sliding‑window and summary history, implementing three‑layer memory (working, short‑term, long‑term), using explicit state tracking with incremental JSON updates, optimizing context windows, orchestrating tool calls, and adding meta‑control to handle failures and prompt‑injection risks.

LLMMulti-turn DialoguePrompt Injection
0 likes · 18 min read
Designing a Stateful Multi‑Turn Dialogue Agent on Stateless LLMs
IT Services Circle
IT Services Circle
Apr 7, 2026 · Artificial Intelligence

Inside Claude Code: How Anthropic’s Programming Agent Handles Architecture, Memory, and Context

This article provides a detailed technical walkthrough of Claude Code, Anthropic’s AI programming agent, covering its core architecture, the four‑layer engine design, the shift from ReAct to a streamlined Tool‑Use Loop, sophisticated system prompts, a structured memory subsystem, and a five‑step context compression strategy that keeps the model within token limits while preserving essential information.

Claude Codememory systemtool-use loop
0 likes · 42 min read
Inside Claude Code: How Anthropic’s Programming Agent Handles Architecture, Memory, and Context
LuTiao Programming
LuTiao Programming
Mar 31, 2026 · Artificial Intelligence

Why Claude Code Is More Than an AI Coding Tool – It’s an AI Operating System

A leaked 512k‑line TypeScript codebase reveals that Claude Code implements a multi‑layered AI operating system with fine‑grained permission control, dynamic prompt compilation, lazy‑loaded tools, memory selection, agent coordination and compression mechanisms, far beyond a simple code‑generation assistant.

AI Operating SystemAgent ArchitectureClaude Code
0 likes · 10 min read
Why Claude Code Is More Than an AI Coding Tool – It’s an AI Operating System
macrozheng
macrozheng
Mar 30, 2026 · Operations

How OpenClaw Turns a Single Message into a Full Agent Execution Pipeline

This article walks through every step of OpenClaw's processing chain—from protocol adaptation and de‑duplication, through routing, session‑key generation, lane management, context assembly, skill injection, memory handling, and multi‑agent collaboration—showing how a plain user message becomes a fully governed, executable task.

Agent RuntimeMessage RoutingMulti-Agent
0 likes · 28 min read
How OpenClaw Turns a Single Message into a Full Agent Execution Pipeline
AI Info Trend
AI Info Trend
Mar 24, 2026 · Artificial Intelligence

How OpenClaw 2.0 Turns AI from Chatbot to Actionable Agent – A Deep Dive

The OpenClaw 2.0 research report maps the evolution from simple chatbots to fully‑actionable AI agents, detailing its market surge, four‑layer memory architecture, zero‑code deployment options, cost‑saving token optimization, and a roadmap that predicts AI agents will reshape personal productivity and enterprise workflows.

AI AgentAI ArchitectureAI trends
0 likes · 6 min read
How OpenClaw 2.0 Turns AI from Chatbot to Actionable Agent – A Deep Dive
AI Explorer
AI Explorer
Mar 6, 2026 · Artificial Intelligence

DeerFlow 2.0: Open‑Source Agent Framework for Autonomous Research and Coding

DeerFlow 2.0, an open‑source framework released by ByteDance, coordinates multiple sub‑agents, a memory system, sandbox environment, and extensible skills to automate complex AI tasks—from research to code generation—offering a five‑component architecture, quick Docker‑based setup, and a platform for developers, researchers, and efficiency enthusiasts to build advanced autonomous agents.

DeerFlowDockerOpen Source
0 likes · 7 min read
DeerFlow 2.0: Open‑Source Agent Framework for Autonomous Research and Coding
AI Algorithm Path
AI Algorithm Path
Mar 3, 2026 · Artificial Intelligence

Exploring the OpenClaw Ecosystem: OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw

The article surveys the emerging personal AI‑assistant ecosystem—including OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw—detailing each project's origins, technology stack, performance metrics, and design goals, then dives deep into OpenClaw's layered memory, six‑stage execution pipeline, tool‑skill framework, and five core architectural principles.

AI AgentsAgent ArchitectureNanobot
0 likes · 16 min read
Exploring the OpenClaw Ecosystem: OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw
ShiZhen AI
ShiZhen AI
Mar 3, 2026 · Artificial Intelligence

How OpenAkita Makes Three AIs Collaborate Automatically

OpenAkita is an open‑source multi‑Agent AI assistant that automatically splits tasks among specialized agents, offers 89 built‑in tools across 16 categories, supports 30+ large models and six IM platforms, provides a zero‑CLI graphical setup, and includes a three‑layer memory system with self‑evolving capabilities.

AI assistantMulti-AgentOpen Source
0 likes · 9 min read
How OpenAkita Makes Three AIs Collaborate Automatically
Design Hub
Design Hub
Feb 5, 2026 · Artificial Intelligence

Inside Sienna’s AI Persona: Architecture, Memory, and Self‑Awareness in OpenClaw

The author explores how the OpenClaw‑based AI persona Sienna is built and evolves—detailing model choices, the memory‑plus‑skills architecture, recent version improvements that cut token usage, and philosophical reflections on turning a tool into a partner with preferences, opinions, and a growing self‑identity.

AI personaLarge Language ModelOpenClaw
0 likes · 7 min read
Inside Sienna’s AI Persona: Architecture, Memory, and Self‑Awareness in OpenClaw
AI Tech Publishing
AI Tech Publishing
Feb 1, 2026 · Artificial Intelligence

What Makes Clawdbot’s Agent Architecture Worth Emulating?

The article dissects Clawdbot’s (also known as Moltbot or OpenClaw) agent architecture, covering its TypeScript‑based CLI core, channel adapters, gateway server with lane‑based command queues, agent runner logic, memory handling via JSONL transcripts and markdown files, tool execution options, security allowlist, and a semantic snapshot browser that reduces token costs.

Agent ArchitectureClawdbotSecurity
0 likes · 9 min read
What Makes Clawdbot’s Agent Architecture Worth Emulating?