HARNESS, SCAFFOLD, AND THE AI AGENT TERMS WORTH GETTING RIGHT

Summary from the TLDR Dev newsletter

An AI agent is defined as the combination of a core model with a behavior-defining scaffold and an execution-focused harness. These systems use context engineering and sub-agents to manage memory and break down complex objectives into specialized, autonomous tasks. In training scenarios, reinforcement learning structures like rollouts and rewards provide the essential data and scoring mechanisms needed to refine model weights through environmental interaction.