A new glossary from Hugging Face aims to clarify the increasingly muddled vocabulary surrounding AI agents, where terms like "harness" and "scaffold" are often used inconsistently across different frameworks and contexts. Authored by Sergio Paniego and Aritra Roy Gosthipaty, it defines key concepts such as model, scaffolding, context engineering, tool use, and training-related terms to provide a practical mental model for newcomers and practitioners alike. Rather than enforcing a single correct definition, the piece seeks to make technical discussions about building, deploying, and training agents easier to follow.

The article provides a glossary of key terminology in the rapidly evolving AI agents field, clarifying commonly confused terms like "harness" and "scaffold." According to the authors, the model (LLM) is the core text-processing engine, scaffolding defines the behavioral layer around it through prompts and tool descriptions, and a harness executes tools and manages the agent's loop. The piece aims to establish practical definitions for these terms to facilitate clearer communication among practitioners building, deploying, or using AI agents, while acknowledging that universal definitions don't yet exist across different frameworks.