Ai Agents

Introduction Retrieval-Augmented Generation (RAG) transformed how LLMs access external knowledge. But traditional RAG has a fundamental limitation: it’s passive. You retrieve once, hope it’s relevant, and generate an answer. If the retrieval fails, the entire system fails. Agentic RAG changes this paradigm. Instead of a single retrieve-then-generate pass, an AI agent actively plans retrieval strategies, evaluates results, reformulates queries, and iterates until it finds sufficient information—or determines that it cannot. ...

Introduction Claude Code introduces a powerful feature called Skills—a way to teach Claude repeatable, specialized capabilities that persist across sessions. Think of Skills as plugins for behavior: structured instruction sets that define exactly what Claude should do, when to do it, and which tools it can use. Unlike one-off prompts that you type into chat, Skills are persistent, discoverable, and automatically selected by Claude based on context. They transform Claude from a general-purpose assistant into a specialized agent that can reliably perform complex, domain-specific tasks. ...

As large language models become more capable, the challenge shifts from “can they reason?” to “can they act?” The Model Context Protocol (MCP) is Anthropic’s answer to this question—a standardized way for LLMs to discover, understand, and safely use tools, data, and actions in the real world. Before MCP, every AI application required custom integrations, hard-coded tool definitions, and fragile glue code. MCP changes this by providing a universal protocol that any LLM can use to interact with external systems. ...

This tutorial explains how modern AI agents (like those in Cursor, IDE copilots, and autonomous coding tools) create, maintain, and execute to-do lists — and how you can build the same capability from scratch to production. This is not a UX trick. A to-do list is the core cognitive control structure that turns a language model from a chatty assistant into an agent that finishes work. 1. Why To-Do Lists Matter for Agents Large Language Models (LLMs) do not naturally: ...

This guide is a comprehensive, production-grade walkthrough for building Agent-to-Agent (A2A) systems — from first principles to real-world deployment. It is written for engineers who already understand APIs, cloud infrastructure, and LLMs, but are new to multi-agent interoperability. The focus is on practical engineering, not demos. 1. What Is Agent-to-Agent (A2A)? A2A (Agent-to-Agent) is an architectural pattern and emerging protocol standard that enables autonomous software agents to: Discover each other Advertise capabilities Exchange structured tasks Stream intermediate progress Exchange artifacts and results Operate independently across services, teams, or organizations Think of A2A as: ...