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Architecture and Beyond
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Architecture and Beyond

Focused on AIGC SaaS technical architecture and tech team management, sharing insights on architecture, development efficiency, team leadership, startup technology choices, large‑scale website design, and high‑performance, highly‑available, scalable solutions.

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Recent Articles

Latest from Architecture and Beyond

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Architecture and Beyond
Architecture and Beyond
Mar 29, 2026 · Artificial Intelligence

Designing Efficient Memory for Claude Code: Typed Storage, Indexed Management, Triggered Retrieval, and Pre‑Use Validation

This article analyzes Claude Code's memory system, explaining how typed storage separates user, feedback, project, and reference data, how an indexed MEMORY.md file keeps the index lightweight, how triggered retrieval balances relevance, freshness, and reliability, and why pre‑use validation prevents stale or incorrect facts from contaminating model responses.

AI MemoryClaudepre‑use validation
0 likes · 17 min read
Designing Efficient Memory for Claude Code: Typed Storage, Indexed Management, Triggered Retrieval, and Pre‑Use Validation
Architecture and Beyond
Architecture and Beyond
Mar 7, 2026 · Artificial Intelligence

Effective Context Transfer in Multi‑Agent Systems: Strategies and Pitfalls

Choosing how to pass context between agents determines system stability, token cost, and debugging difficulty; the article defines context, categorizes four context types, and evaluates four main strategies—shared state, message passing, context compression, and hierarchical routing—detailing mechanisms, use‑cases, implementation pitfalls, and cost‑effectiveness trade‑offs.

LLMagent-routingcontext-passing
0 likes · 20 min read
Effective Context Transfer in Multi‑Agent Systems: Strategies and Pitfalls
Architecture and Beyond
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.

Knowledge BaseLong-Term Memoryagent design
0 likes · 18 min read
Designing Scalable Long-Term Memory for AI Agents: Capture, Compress, Retrieve
Architecture and Beyond
Architecture and Beyond
Feb 1, 2026 · Artificial Intelligence

5 High‑ROI Strategies to Supercharge RAG Retrieval Performance

This article outlines five practical engineering strategies—multi‑vector retrieval, manual splitting and labeling, scalar enhancement, context augmentation, and dense‑sparse vector integration—that together address common RAG retrieval bottlenecks and dramatically improve recall stability and answer quality.

BM25LLMRAG
0 likes · 17 min read
5 High‑ROI Strategies to Supercharge RAG Retrieval Performance
Architecture and Beyond
Architecture and Beyond
Jan 17, 2026 · Artificial Intelligence

Progressive Disclosure & Dynamic Context: Making LLM Agents Reliable Execution Systems

This article explains how progressive disclosure and dynamic context management address the three core bottlenecks of complex LLM agents—context explosion, tool overload, and uncontrolled execution—by structuring context, tools, and SOPs into layered, token‑efficient, and verifiable workflows.

AI engineeringDynamic ContextLLM agents
0 likes · 15 min read
Progressive Disclosure & Dynamic Context: Making LLM Agents Reliable Execution Systems
Architecture and Beyond
Architecture and Beyond
Jan 10, 2026 · Artificial Intelligence

How to Systematically Test and Evaluate Industry AI Agents

This guide explains how to systematically evaluate industry‑specific AI agents by testing the combined model and engineering stack, building domain‑expert‑driven datasets, designing reproducible testing systems, managing assets, controlling costs, and applying both traditional and LLM‑based methods to ensure reliable, stable performance.

AI evaluationAgent TestingLLM testing
0 likes · 20 min read
How to Systematically Test and Evaluate Industry AI Agents
Architecture and Beyond
Architecture and Beyond
Dec 27, 2025 · Artificial Intelligence

Turning Claude Skill Folders into Scalable Industry Workflows

This article explains how Anthropic's Claude Skill folders let you package domain expertise, scripts, and resources into reusable modules, differentiate Skills from prompts, combine them with MCP tools and workflows, and build a robust mixed Agent‑Workflow architecture for reliable enterprise automation.

AI AgentsClaudeMCP
0 likes · 18 min read
Turning Claude Skill Folders into Scalable Industry Workflows
Architecture and Beyond
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.

Industry AIKnowledge RetrievalRAG
0 likes · 17 min read
Designing RAG for Industry‑Specific AI Agents: From Data to Safe Execution
Architecture and Beyond
Architecture and Beyond
Dec 7, 2025 · Artificial Intelligence

How to Turn Industry Workflows into Actionable Skills with Claude

This article explains Claude's Skill system, how Skills differ from prompts, the concept of industry Workflows, the boundaries between Skills, Tools, and MCPs, and provides a step‑by‑step guide for designing Skills, wrapping legacy systems into Workflows, and building a hybrid Workflow‑plus‑Agent architecture for reliable, auditable automation.

AIAgentClaude
0 likes · 18 min read
How to Turn Industry Workflows into Actionable Skills with Claude