  Battery-less embedded devices powered by energy harvesting are increasingly
being used in wireless sensing applications. However, their limited and often
uncertain energy availability challenges designing application programs. To
examine if BDI-based agent programming can address this challenge, we used it
for a real-life application involving an environmental sensor that works on
energy harvested from ambient light. This yielded the first ever implementation
of a BDI agent on a low-power battery-less and energy-harvesting embedded
system. Furthermore, it uncovered conceptual integration challenges between
embedded systems and BDI-based agent programming that, if overcome, will
simplify the deployment of more autonomous systems on low-power devices with
non-deterministic energy availability. Specifically, we (1) mapped essential
device states to default \textit{internal} beliefs, (2) recognized and
addressed the need for beliefs in general to be \textit{short-} or
\textit{long-term}, and (3) propose dynamic annotation of intentions with their
run-time energy impact. We show that incorporating these extensions not only
simplified the programming but also improved code readability and understanding
of its behavior.
