Ai Agents

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: ...

Introduction Agentic workflows move AI beyond one-shot prompting into iterative, autonomous problem-solving by letting agents plan, act, observe, and refine—much like a human tackling a complex task. This shift yields more reliable, adaptable, and goal-directed systems for real-world, multi-step problems. In this article I explain the five core agentic workflow patterns (Reflection, Tool Use, ReAct, Planning, and Multi-Agent), show how they combine, give practical implementation guidance, example architectures, and discuss trade-offs and evaluation strategies. ...