How Modern AI Agents Work: Harness, Tools, Skills, Memory and MCP Explained

Modern AI agents have evolved beyond simple chatbots into systems capable of reasoning, planning, and autonomously completing complex tasks using multiple integrated components. A production AI agent combines a language model with a harness, tools, skills, memory, and Model Context Protocol (MCP) integrations, each serving a distinct role. The agent harness acts as the execution layer, coordinating task management, tool access, security, and memory retrieval to bridge the gap between reasoning and real-world action. Tools allow agents to interact with external systems like databases and APIs, while MCP provides a standardized protocol for discovering and connecting to those systems without custom-built integrations for each one. Skills add a higher layer of reusable workflows and domain knowledge, enabling agents to handle sophisticated, multi-step tasks efficiently.