AI/MCP Concepts

AI/MCP concepts focus on the principles and technologies behind Artificial Intelligence (AI) and Machine-Controlled Processes (MCP). These include automation, machine learning, decision-making algorithms, and intelligent systems that enhance operational efficiency and reduce human error.

Categories:

Blended Identity

AI/MCP Concepts

Blended Identity refers to a modern identity model for user-driven AI agents in which the agent operates using a composite identity derived from two simultaneous sources of trust: The agent’s own workload identity (cryptographically verifiable, rooted in a trust provider), and The identity of the human user currently engaging or instructing the AI agent. The combination produces a dynamic, runtime-only identity that shapes what the agent can do, enforces least privilege, and preserves full accountability for user-initiated actions taken by the agent. What makes Blended Identity unique is that, although anchored in workload identity, it must also integrate with an enterprise’s workforce identity system to generate this composite identity just-in-time at the moment of invocation.

Self-RAG

AI/MCP Concepts

Self-RAG (Self-Retrieval Augmented Generation) is an emerging AI architecture in which a model autonomously retrieves, filters, and evaluates its own contextual information during the generation process, without relying on an external retriever service. It merges retrieval and reasoning within the model itself, allowing for adaptive, self-supervised access to relevant knowledge or memory.

Retrieval-Augmented Generation (RAG)

AI/MCP Concepts

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models (LLMs) by combining real-time information retrieval with generative reasoning. Instead of relying solely on pre-trained model knowledge, RAG systems query external data sources, retrieve relevant content, and feed it into the model’s prompt context to generate accurate, up-to-date, and domain-specific responses.

Multi-Agent System (MAS)

AI/MCP Concepts

A Multi-Agent System (MAS) is a coordinated network of autonomous AI agents that communicate and collaborate to accomplish shared goals. Each agent operates independently, perceiving its environment, making decisions, and taking actions, but the system as a whole behaves collectively, distributing intelligence across agents rather than centralizing it in one model.

Agent-to-Human Handoff

AI/MCP Concepts

An agent-to-human handoff is the seamless transition of a task or interaction from an autonomous AI agent to a human operator when the agent reaches scope, capability, or accountability limits.

Generative AI

AI/MCP Concepts

Generative AI refers to systems that can create new content, such as text, images, code, or audio, based on patterns learned from large datasets. Unlike traditional predictive AI that classifies or forecasts, generative AI produces original outputs in response to prompts or contextual inputs.

Large Language Model (LLM)

AI/MCP Concepts

MCP Server

AI/MCP Concepts

An MCP Server is the central service in the Model Context Protocol (MCP) ecosystem that exposes tools, data sources, or APIs to authorized MCP Clients. It acts as the authoritative endpoint responsible for managing capabilities, handling authentication, and responding to agent or model requests in a standardized, interoperable format.

MCP Host

AI/MCP Concepts

An MCP Host is the environment or runtime that runs a Model Context Protocol (MCP) server and provides tools, data, or services that AI agents and models can access through standardized interfaces. It acts as the provider side in the MCP ecosystem, exposing actions, endpoints, and contextual data to authorized MCP clients.

MCP Client

AI/MCP Concepts

An MCP Client is the software component or AI agent that connects to a Model Context Protocol (MCP) server to request tools, data, or context. It serves as the initiator in an MCP workflow, sending structured requests, receiving context, and invoking actions defined by MCP-compatible tools.