AI Glossary for Beginners

Have you noticed that AI (Artificial Intelligence) seems to be everywhere these days? It’s embedded in the tools and products we use every day. However, once discussions dive into the technical side—terms like Agent, Knowledge Base, and MCP—things can get confusing.

This article is for you—the AI beginner. My goal is to help you build a clear and reliable understanding of essential AI terminology. I’ll explain each concept in simple, straightforward language so you can grasp their real-world functions.

Once you master these basics, you’ll be able to understand AI news, product features, and confidently join the conversation.

Agent

One-Sentence Definition

An Agent is like a “goal-oriented digital helper that figures out how to get things done on its own.” It’s not just a Q&A bot—it’s a program capable of planning and executing a series of complex actions autonomously.

How It Works

An AI agent is called “intelligent” because it exhibits autonomy, following four core steps:

1. Goal Setting: Receives your command, e.g., “Plan a five-day trip to Tokyo with a ¥10,000 budget.”
2. Planning & Decomposition: Breaks the task into smaller steps—search flights → filter hotels → design daily itinerary → book activities.
3. Action & Execution: Uses external tools (like browsers, map APIs, or booking systems) to complete each step.
4. Reflection & Correction: Constantly checks whether results match expectations. If something fails (e.g., a hotel booking), it re-plans and finds an alternative.

Think of it as your personal assistant—you give a broad request, and it independently thinks, uses tools, and completes the process.

Applications:

• Workflow Automation: Automating complex, repetitive business processes such as data aggregation and report generation.
• AI Coding Assistants: Understanding a functional requirement and autonomously completing coding, testing, and debugging tasks.

Technical Core

An Agent’s autonomy relies on three key components:

1. Large Language Model (LLM): The “brain”—handles reasoning, planning, and decision-making.

2. Memory: Prevents “forgetfulness.”

   • Short-term memory keeps conversational context.
   • Long-term memory stores past learnings and experiences.

3. Tooling: The “hands and feet.” Agents use tools like search engines, code interpreters, or APIs to act on their plans.

Product Examples

• Digital Enterprise Agents: Automating HR, finance, and customer service workflows.
• Personal Automation Agents: Managing calendars, emails, and tasks across multiple apps.
• General Agent Platforms: Frameworks for developers to build customized agents for specific tasks.

Knowledge Base

One-Sentence Definition

A Knowledge Base is an AI system’s external long-term memory and professional reference library. It stores structured facts and domain knowledge to keep AI responses accurate and up to date.

Why It Matters

LLMs have two major limitations:

1. Knowledge Cutoff: Training data stops at a specific date (e.g., early 2023).
2. Limited Domain Expertise: Hard to retain deep, industry-specific information.

A knowledge base solves these by letting AI query external data in real time, ensuring precise, current answers.

Imagine a super-smart friend:

• The friend (LLM) can reason and communicate fluently.
• The encyclopedia (Knowledge Base) lets them reference the latest information to give factual, reliable answers.

Technical Core — RAG (Retrieval-Augmented Generation)

1. Retrieve: AI searches the knowledge base for relevant documents.
2. Augment: The retrieved “evidence” is combined with the question.
3. Generate: The LLM produces an answer based on verified information—fact-driven, not fabricated.

Applications

• Smart Customer Support: Pulling from FAQs, manuals, and policies.
• Internal Knowledge Assistants: Helping employees query company processes and project data.
• Legal/Medical Q&A: Referencing up-to-date laws or research papers.

MCP (Model Context Protocol)

One-Sentence Definition

MCP (Model Context Protocol) is a set of rules and standards that dictate how an AI system (like an Agent) should organize, exchange, and maintain all contextual information during task execution.

Why It Matters

MCP addresses the challenges of memory continuity and data communication in AI workflows:

1. Workflow Coherence: Keeps multi-step tasks consistent and connected.
2. Data Uniformity: Ensures seamless information exchange between Agents, tools, and databases.
3. Efficiency: Manages LLM context limits efficiently by prioritizing key information.

Imagine an Agent as a project manager who maintains a “Project Execution Protocol (MCP)” log—defining goals, completed steps, issues, and next actions. Every team member (tools, other agents) must update the log in a consistent format, ensuring smooth collaboration.

Technically, MCP defines data schemas and communication formats (e.g., JSON) to structure Agents’ short-term memory and tool usage logs.

Applications

• Multi-Agent Collaboration: Enabling agents to hand off tasks seamlessly.
• RAG Optimization: Structuring retrieved data and metadata for precise LLM integration.
• Long-Form Automation: Supporting workflows that span dozens of steps while retaining full task context.