AI Agents Glossary
Your practical reference for AI agent terminology. Use this glossary to align teams on definitions, architecture choices, and implementation patterns.
How to Use This Glossary
If you are evaluating tools, start with foundational terms before diving into framework comparisons. If you are building production workflows, prioritize terms tied to architecture and reliability decisions.
- Start with definitions to align language across product, engineering, and operations.
- Use linked tutorials to translate terms into implementation steps.
- Use linked comparisons and reviews to map concepts to platform tradeoffs.
- Use related terms to build a structured learning path instead of isolated reading.
Suggested Learning Paths
Beginner Path
Read AI Agents, Agentic AI, and Tool Calling, then continue with Build Your First AI Agent.
Architecture Path
Focus on AI Agent Memory, AI Agent Orchestration, and AI Agent Guardrails, then review AI Agent Architecture.
Platform Decision Path
Read Multi-Agent Systems and LLM Agents, then compare options in Best AI Agent Platforms.
W
What Is Human-in-the-Loop AI?
A practical explanation of human-in-the-loop AI — approval checkpoints in agent workflows, when to require human confirmation, HITL patterns in Lan...
Read Term →
What Is Prompt Chaining?
A clear explanation of prompt chaining — how to link multiple LLM calls where the output of one becomes the input of the next, when to use it versu...
Read Term →
What Is Prompt Injection in AI Agents?
Prompt injection is an attack where malicious instructions embedded in external content hijack an AI agent's behavior.
Read Term →
What Is Retrieval-Augmented Generation (RAG)?
Learn how retrieval-augmented generation (RAG) works in AI agents, including data retrieval pipelines, grounding strategies, and production quality...
Read Term →
What Is Structured Output in AI Agents?
Structured output is how AI agents return machine-readable data instead of free-form text, enabling reliable parsing, validation, and downstream au...
Read Term →
What Is Task Decomposition in AI Agents?
A practical guide to task decomposition in AI agents — how to break complex goals into subtasks, hierarchical planning, parallel versus sequential...
Read Term →
What Is the Agent Loop?
Understand the AI agent loop — the perceive, think, act, and observe cycle that drives autonomous agent behavior, including ReAct patterns, stoppin...
Read Term →
What Is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard that connects AI agents to external data sources, tools, and APIs through a unified JSON-RPC i...
Read Term →
What Is Tool Calling in AI Agents?
Learn how tool calling works in AI agents, including function execution, schema validation, error handling, and safe production deployment patterns.
Read Term →
What Is Tool Use in AI Agents?
Tool use is the capability that lets AI agents extend beyond text generation by calling external functions, APIs, and services.
Read Term →Showing 25-34 of 34
Frequently Asked Questions
Who should use this AI agents glossary?
This glossary is built for operators, product managers, developers, and business teams who need practical definitions tied to implementation decisions, not just dictionary-style summaries.
How should I read glossary terms if I am new to AI agents?
Start with foundational terms such as AI Agents, Agentic AI, and Tool Calling, then move to architecture topics like Memory, Orchestration, and Guardrails before comparing specific frameworks.
Are glossary pages connected to tutorials and comparisons?
Yes. Every glossary term includes internal links to related tutorials, comparisons, reviews, and templates so you can move from concept to implementation quickly.
How often are glossary terms updated?
Glossary entries are reviewed as platform patterns evolve, especially for orchestration, retrieval, memory, and governance topics where implementation standards change quickly.