  Simulation-based verification is beneficial for assessing otherwise dangerous
or costly on-road testing of autonomous vehicles (AV). This paper addresses the
challenge of efficiently generating effective tests for simulation-based AV
verification using software testing agents. The multi-agent system (MAS)
programming paradigm offers rational agency, causality and strategic planning
between multiple agents. We exploit these aspects for test generation, focusing
in particular on the generation of tests that trigger the precondition of an
assertion. On the example of a key assertion we show that, by encoding a
variety of different behaviours respondent to the agent's perceptions of the
test environment, the agency-directed approach generates twice as many
effective tests than pseudo-random test generation, while being both efficient
and robust. Moreover, agents can be encoded to behave naturally without
compromising the effectiveness of test generation. Our results suggest that
generating tests using agency-directed testing significantly improves upon
random and simultaneously provides more realistic driving scenarios.
