The subjective sense that an AI agent "works well" can mislead: it rests on the most recent or most memorable interactions, not on an overall view of its performance. Measuring reliability objectively calls for indicators gathered systematically over time.
Action success rate
The proportion of actions completed successfully against total attempts is a direct indicator: an agent that frequently fails on a certain kind of request marks an area to improve, even if the successful requests feel broadly positive.
How often humans have to correct it
How often does a user have to correct the result of an agent's action manually before accepting it? A high number of corrections indicates the agent is not genuinely automating the task, only producing a draft that always needs substantial revision.
Consistency over time, not a snapshot
An agent can perform well at one moment and less well at another, depending on variations in the context supplied or the data available. Monitoring reliability over an extended period, rather than a limited sample of recent interactions, gives a more realistic picture of how far the system can be trusted.