Harness

A harness surrounds an AI model with workflow control, tool access and state management so it can perform bounded tasks; components vary by application.

Longer tasks need a dependable operating framework

Proposal preparation may involve several research steps, questions and revisions. After an interruption, the application must retain what is complete, which assumptions remain open and what action is permitted next. A harness organises these processes around the model. It can help the application continue from an explicit working state instead of reconstructing important decisions from a long conversation. The term does not imply a uniform feature set, however. The actual architecture must show which responsibilities the framework handles.

Recovery deserves its own acceptance test

A useful test interrupts work at different points and examines what happens next. Are sources lost, actions duplicated or previous approvals reused after their conditions have changed? Orchestration and observability help organise responsibilities and execution records. Economic benefits may include fewer repeated steps, less rework and more manageable failure consequences. Additional control can also introduce complexity. A pilot therefore needs the smallest framework that handles its real interruptions and risks. An elaborate diagram cannot substitute for a demonstrated recovery that preserves the correct state.

Sources

• Harness design for long-running apps