AgentScope Agent Memory
An agent that forgets what it just tried is doomed to repeat its own mistakes. As covered in the general agent memory architecture post earlier in this series, managing what an agent remembers — and how much of it stays in active context — is one of the harder practical problems in …
Single Agent vs Multi-Agent Architecture
Not every agentic task benefits from more agents. Adding coordination, communication overhead, and additional points of failure is a real cost — one that's only worth paying when a task genuinely benefits from specialization or parallel work that a single agent handles...
Agent Memory Architecture
An agent working through a long, multi-step task needs to remember what it's already tried, what it's learned, and what still needs doing — but an LLM's context window, as covered in the tokens post earlier in this series, is finite. Agent memory architecture is the set of...
Agent Tools
An LLM without tools can only describe what it would do. Give it tools, and it can actually do it — search the web, query a database, run code, send a message. Tools are what turn an agent's reasoning into real-world action, and designing them well is one of the most …
AI Agent vs LLM
It's easy to use "AI agent" and "LLM" interchangeably in casual conversation, but they describe genuinely different layers of a system. An LLM is a component — a powerful one — while an agent is an entire architecture built around that component, adding the pieces that let it...
Building a Multi-Document RAG System
Introduction: From Single File to Knowledge Base A single-document RAG system answers questions about one PDF or text file. A multi-document RAG system answers questions across an entire corpus: hundreds of product manuals, thousands of research papers, or millions of support...
RAG vs Fine-Tuning
Introduction: Two Paths to Customization When organizations want an LLM to perform well on their specific domain, they face a strategic choice between two fundamentally different approaches. Retrieval-Augmented Generation augments the model's context at inference time by...
LangChain Architecture Explained
Layered Design Philosophy LangChain's architecture is deliberately layered, resembling the design of modern web frameworks. Each layer has a specific responsibility, and developers can interact with the framework at whatever level of abstraction suits their needs....