AI Glossary — Key Terms in AI Agents, Automation & Development

Glossary

AI & Agent Technology Glossary

20 key terms explained in plain language. From AI agents to zero-shot learning — understand the technology powering modern businesses.

A C E F H I L M O P R T V

A

AI Agent

An autonomous program that can reason, plan, and execute complex tasks without step-by-step human instructions.

Agentic Workflow

A process where AI agents autonomously execute multi-step tasks, making decisions and using tools without constant human direction.

Autonomous Agent

An AI agent capable of completing complex, multi-step tasks independently with minimal human intervention.

AI-Native

A company or product built from the ground up with AI as a core capability, not as an add-on to existing processes.

C

Context Window

The maximum amount of text an AI model can process in a single interaction, measured in tokens.

Chain-of-Thought (CoT)

A prompting technique that instructs AI to reason step-by-step before reaching a conclusion, dramatically improving accuracy.

E

Embeddings

Numerical representations of text that capture semantic meaning, enabling AI systems to measure similarity between concepts.

F

Fine-Tuning

Training a pre-trained AI model on specialized data to improve its performance on specific tasks or domains.

Function Calling

The ability of AI models to invoke external functions or APIs by generating structured outputs that match predefined schemas.

H

Hallucination

When an AI model generates plausible-sounding but factually incorrect information with unwarranted confidence.

Human-in-the-Loop (HITL)

A design pattern where human judgment is integrated into AI workflows at critical decision points for quality control and oversight.

I

Inference

The process of running a trained AI model to generate predictions or outputs from new inputs.

L

Large Language Model (LLM)

A neural network trained on massive text data that can understand and generate human-like language, code, and reasoning.

M

Multi-Agent System

An architecture where multiple AI agents collaborate, each with specialized roles, to accomplish complex tasks that no single agent could handle alone.

O

Orchestration

The coordination layer that manages multiple AI agents, routing tasks, handling dependencies, and ensuring quality across the system.

P

Prompt Engineering

The practice of crafting instructions that guide AI models to produce accurate, relevant, and useful outputs.

R

Retrieval-Augmented Generation (RAG)

A technique that gives AI models access to external knowledge by retrieving relevant documents before generating a response.

T

Token

The basic unit of text that AI models process — roughly equivalent to a word or word fragment.

Tool Use

The capability of AI agents to interact with external software, APIs, and systems to accomplish tasks beyond text generation.

V

Vector Database

A specialized database optimized for storing and querying embedding vectors, enabling fast semantic search at scale.

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