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Compared with conventional wireless sensor networks (WSNs) operating based on the client-server computing model, mobile agent (MA)-based WSNs can facilitate agent-based data aggregation and energy-efficient data collection. In MA systems, it has been known that finding the optimal itinerary of an MA is nondeterministic polynomial-time hard (NP-hard) and is still an open area of research. In this paper, we consider the impact of both data aggregation and energy efficiency in itinerary selection. We first propose the Itinerary Energy Minimum for First-source-selection (IEMF) algorithm. Then, the itinerary energy minimum algorithm (IEMA), which is the iterative version of IEMF, is described. This paper further presents a generic framework for the multiagent itinerary planning (MIP) solution, i.e., the determination of the number of MAs, allocating a subset of source nodes to each agent and itinerary planning for each MA. Our simulation results have demonstrated that IEMF provides higher energy efficiency and lower delay, compared with existing single-agent itinerary planning (SIP) algorithms, and IEMA incrementally enhances IEMF at the cost of computational complexity. The extensive experiments also show the effectiveness of MIP algorithms when compared with SIP solutions.