Agents / Standard term
Agent harness
All the software wrapped around an AI model that turns raw intelligence into a reliable, controllable product: permissions, retries, logging, memory, state tracking, and error handling.
An agent harness is all the software wrapped around an AI model that makes its outputs safe and dependable in production: permissions, retries, logging, memory, state tracking, and error handling. When an agent tries to call a tool, the harness checks permissions, validates the arguments, runs the call, handles failures with retries, logs what happened, and tracks conversation state. Think of the model as a skilled worker. The harness is the safety equipment, scheduling system, and quality-control process around them.
Builder example
When an AI product fails in the real world, the problem is almost always in the harness: a missing retry that turns a temporary API error into a crashed workflow, a permission gap that lets the agent access restricted data, or no logging to figure out what went wrong. Investing in your harness pays off more than switching to a smarter model, because reliability is what makes users trust the product.
Common confusion: A model demo and a production product are very different things. The model might ace a task in a demo, then fail repeatedly in production because the harness lacks retries, permission checks, or graceful error handling.