An AI agent harness is the software infrastructure that enables large language models to take action on tasks by providing tools, memory, execution environments, and safety controls. Databricks frames it as Agent = Model + Harness, where the model supplies reasoning while the harness handles execution through core components including system prompts, tools, sandboxes, storage, memory management, feedback loops, guardrails, and observability. The article argues that harness quality increasingly determines real-world agent performance more than the underlying model itself, with effective enterprise governance requiring centralized control over data access and evaluation systems.
