AgentScope Agent Communication
The moment a system has more than one agent, a new problem appears that single-agent design doesn't have to deal with: how do agents actually talk to each other, in a way that stays coherent, traceable, and debuggable? AgentScope addresses this by extending the same core...
What is AgentScope?
Among the growing field of frameworks for building AI agents — LangChain, CrewAI, AutoGen, and others — AgentScope has carved out a distinct identity around one core principle: transparency. Developed by Alibaba's research team, it's an open-source Python framework built...
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...
Building an AI CRM Agent
A sales or support team's CRM holds a huge amount of valuable, structured information — contacts, deal stages, communication history, ticket status — but keeping it accurate and acting on it consistently is a constant manual burden. An AI CRM agent applies the agent pattern...
Building a Coding Agent
Coding is one of the domains where AI agents genuinely shine — not because writing code is easy for an LLM, but because code has something rare among agent tasks: a built-in, objective way to check if the work actually succeeded. Tests pass or they don't; code runs or it throws …
Langchain
Building My Personal Blog with LangChain: A Practical Journey into AI-Powered Content When I first decided to build a personal blog, I didn’t want just another static website. I wanted something smarter—something that could interact, assist, and evolve with readers. That’s...
Autonomous AI Agents
Some AI agents wait for a human to approve every action. Others run for hours, making dozens of decisions on their own, only checking in when something genuinely needs human judgment. That second category — agents operating with minimal ongoing human oversight — is what's...
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...
What is an AI Agent?
A chatbot answers your question and stops. An AI agent, by contrast, can decide what to do, take an action, observe the result, and decide what to do next — repeating that cycle until it's actually accomplished a goal. This shift, from generating a single response to...
RAG with MongoDB
Retrieval-augmented generation needs somewhere to store and search the embeddings it retrieves context from — and increasingly, teams are choosing to keep that vector data in the same database already holding their application's operational data, rather than standing up a...
AI Agent Directory - Few Shots LLM Models
The AI agent ecosystem is growing fast. Here's a quick directory of notable AI startups and a couple of few-shot LLM models worth knowing about. Two lines each — just enough to know what they do and why they matter. AI Agent Directory (Test) Can of Soup — An AI-powered app that …
Vector Search Performance Optimization
A vector search system that performs beautifully in a demo with a thousand test vectors can slow to a crawl once it's handling millions of embeddings and real production traffic. Getting vector search to perform well at scale isn't one fix — it's a set of levers spanning...
Reducing RAG Hallucinations
Introduction: When RAG Lies Despite the Evidence Retrieval-Augmented Generation was supposed to solve hallucinations. By grounding the model in retrieved documents, we assumed it would stick to the facts. But RAG systems hallucinate too. They ignore retrieved context and...
Improving RAG Retrieval
Introduction: The Retrieval Bottleneck The generation phase of RAG receives most of the attention. Developers obsess over prompt engineering, model selection, and temperature settings. But generation quality is fundamentally bounded by retrieval quality. If the retrieved...
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...
Building a PDF RAG System
Introduction: The Most Common RAG Use Case PDF documents are the lingua franca of enterprise knowledge. Contracts, research papers, technical manuals, financial reports, and legal briefs all arrive in PDF format. Building a RAG system that can ingest PDFs, answer questions...
Semantic Search Explained
Introduction: Beyond Keyword Matching For decades, information retrieval was dominated by keyword search. Users typed words, and systems returned documents containing those words. This approach works well for known-item searches, where the user knows exactly what terms to...
Exact Search vs Approximate Search
Every vector search system faces the same fundamental choice: guarantee the mathematically correct answer, or accept a very good answer in exchange for much greater speed. This is the exact-versus-approximate search trade-off, and picking the right side of it — often for...
What is a Vector Database?
Introduction: Databases for the AI Era Traditional databases are designed for exact matching and range queries. They store rows of structured data and retrieve them based on precise conditions. A SQL query like SELECT * FROM products WHERE price < 100 is fast and...