Practical Glossary for AI Agent Terminology (Harness, Scaffold, Agent, etc.)

A new blog post on Hugging Face aims to cut through the confusion around AI agent terminology. The glossary covers terms like Harness, Scaffold, and Agent with simple definitions and real examples drawn from common agent frameworks.

Key Terms Defined

• Agent: A system that uses an LLM to decide actions and execute them (e.g., via tool calls). The blog distinguishes between a 'raw' agent and an agent wrapped in a scaffold.
• Harness: The runtime environment that manages agent lifecycle, including tool registration and execution. Think of it as the OS for your agent.
• Scaffold: The template or framework code that structures how an agent interacts with tools and memory. It's distinct from the agent itself — you can swap scaffolds without altering the agent core.
• Tool: A function the agent can invoke (e.g., a calculator, web search, or database query).
• Memory: Persistent context storage across turns or sessions. The glossary covers short-term (conversation history) vs. long-term (vector store) memory.
• Orchestrator: Coordinates multiple agents or sub-agents, often in a hierarchical setup.

The post also clarifies how these terms map to popular frameworks like LangGraph, CrewAI, and Autogen, helping developers choose the right abstraction layer for their use case.

Why This Matters

As agent frameworks proliferate (LangChain, Vercel AI SDK, smolagents), the same concept often goes by different names — or the same name means different things. This glossary provides a lingua franca for developers building multi-step AI workflows.