Tagged articles
memory system
21 articles · Page 1 of 1
openEuler upgrades to Agentic Infra OS with 24.03 LTS SP5, but its four key capabilities — sandbox, KV Cache, memory system, and security governance — were already released in October; the article clarifies architecture layers, performance claims (20ms sandbox startup, 100k sandboxes in 10s) are vendor self-tests, and advises engineers on which stack layers to offload to the OS.
AI infrastructureAgent security governanceAgentic Infra OS
0 likes · 22 min read
This article dissects the 68,558-character system prompt of Claude Fable 5.1, revealing how a production-grade AI agent uses a persistent memory filesystem, 38 JSON Schema tools, defense-in-depth security, strict copyright limits, and a stepwise visual-output router to shift from prompt engineering to context engineering.
AI AgentAgent RoutingClaude Fable
0 likes · 32 min read
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
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 AgentAutomationSkill Marketplace
0 likes · 13 min read
The article dissects Hermes Agent, an open‑source AI framework with 90K GitHub stars, explaining how its three‑layer memory, self‑generated Skills, and offline GEPA optimizer create a compounding intelligence loop that overcomes the common forgetting problem of AI agents.
AI AgentsGEPAHermes Agent
0 likes · 16 min read
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 AgentsObservabilityagent architecture
0 likes · 33 min read
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 AgentsFramework SelectionObservability
0 likes · 6 min read
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
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 Codebackend architectureconcurrency
0 likes · 19 min read
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.
LLMPrompt Injectioncontext window
0 likes · 18 min read
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
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 systemClaude CodeLLM
0 likes · 10 min read
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
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
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
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 AgentsNanoBotOpenClaw
0 likes · 16 min read
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
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 personaOpenClawSelf-Awareness
0 likes · 7 min read
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.
ClawdbotSemantic Snapshotagent architecture
0 likes · 9 min read
This article provides a deep technical walkthrough of Clawdbot’s architecture, covering its TypeScript CLI core, lane‑based command queue, agent runner, memory system with JSONL and vector search, sandboxed computer control, security allowlist, and the semantic snapshot browser tool.
AI AgentClawdbotLLM
0 likes · 13 min read
This article explores how to create a self‑thinking AI data analyst by detailing agent fundamentals, core modules such as planning, memory and tool scheduling, practical development steps, multi‑agent collaboration, evaluation benchmarks, and real‑world examples like stock backtesting.
AI AgentMCPTool Use
0 likes · 35 min read
