termination condition

In agentic AI, the predefined criteria that tell the agent to stop iterating. For example, here are a few possible termination conditions: - The agent successfully completed the goal. - The agent can't use any more resources. - A human-in-the-loop has detected a problem. In reinforcement learning, the conditions that determine when an episode ends, such as when the agent reaches a certain state or exceeds a threshold number of state transitions. For example, in tic-tac-toe(also known as noughts and crosses), an episode terminates either when a player marks three consecutive spaces or when all spaces are marked.

Practitioners refer to termination condition when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.