Basics

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Introduction: Terminology Matters

We’ve all seen articles about “an AI system”, but what does that actually mean? The raw LLM that generates text? The agent that makes decisions? The tools it calls? The infrastructure that makes it all work reliably? Without clear terminology, teams build brittle integrations, set wrong expectations, and misplace trust—expecting intelligence where there’s really just …

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AI System

An AI system is the comprehensive integration of components that collectively deliver artificial intelligence-powered functionality. It combines the generative capabilities of LLM, the reasoning and decision-making processes of agents, the action-enabling capabilities of tools, and the reliability mechanisms of the orchestration layer. Additional infrastructure components, such as integration layers, plugin frameworks, or standardized protocols like …

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AI Agent

An AI agent is a type of AI system that uses a LLM for reasoning and autonomously pursues goals by making decisions, invoking tools, and adapting to feedback. Agents add goal-directed behavior on top of raw model outputs, enabling systems to act in dynamic and flexible ways. Agents can use different mechanisms to access tools, …

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AI Tool

An AI tool is a capability beyond an LLM’s native text generation that an AI agent can invoke to access data, perform actions, or integrate with services. Tools can be connected in different ways: through direct API calls, plugin frameworks, or standardized protocols such as the Model Context Protocol (MCP). Key characteristics Also: A tool …

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Introduction: Terminology Matters

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AI System

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AI Agent

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AI Tool

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MCP Client

An MCP client is the component of the Model Context Protocol (MCP) that allows AI agents or AI systems to discover and invoke tools or resources in a standardized way. Instead of relying on custom integrations, the MCP client provides a common interface so that agents can call tools exposed by MCP servers dynamically and …

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MCP Host

An MCP host is the runtime environment where the AI agent and the MCP client operate. It provides the context in which the agent reasons, invokes tools through clients, and connects to MCP servers. The host is responsible for mediating these interactions, applying permissions, and ensuring that the system runs within a controlled environment. Key …

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MCP Server

An MCP server is the component of the Model Context Protocol (MCP) that exposes tools, data, or resources in a standardized format so they can be consumed by an MCP client and used by an AI agent. It makes capabilities discoverable, interoperable, and reusable across different AI systems without custom integration code. Key characteristics The …

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Agentic System

An agentic system is an AI system built around one or more AI agents that autonomously pursues goals through reasoning, planning, and tool use. While an AI agent represents a single reasoning entity, an agentic system includes the supporting infrastructure such as tool interfaces, integration layers (including MCP client and MCP server), hosts, and the …

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Large Language Model

A large language model (LLM) is a type of AI model trained on massive amounts of text data to predict the most likely next token in a sequence. This simple mechanism enables LLMs to generate fluent text, translate languages, write code, answer questions, and perform a wide variety of reasoning-like tasks. Key characteristics LLMs are …

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Orchestration Layer

An orchestration layer is the coordinating logic that connects LLMs, AI agents, and external components into a reliable AI system. It ensures that tool calls, workflows, and results flow in the right order, with the right safeguards, so the overall system behaves predictably at scale. Key characteristics Unlike an AI agent, which makes decisions about …

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Understanding Consent in MCP

In the Model Context Protocol (MCP), consent is the foundational security mechanism that governs how to delegate authority to AI agents. It ensures any actions they take are authorized, traceable, and revocable. Think of consent as a detailed, enforceable contract that spells out exactly what agents can do on the user’s behalf, going far beyond …

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Understanding Provenance in MCP

In today’s complex AI landscape, provenance in the Model Context Protocol (MCP) is the mechanism that ensures transparency and accountability. It functions as a detailed history for AI workflows, tracking the origin of data, the transformations it undergoes, and the actions performed by AI agents. This comprehensive audit trail is essential for understanding an agent’s …