Mass transport limitations of particulates within conventional microanalytical systems are often cited as the root cause for low sensitivity but can be overcome by directed analyte transport, such as via biomolecular motors or gradient surfaces. An ongoing challenge is the development of materials that are passive in nature (i.e., no external power source required), but can reconfigure to perform work, such as transporting particle‐based analytes. Mimicking biology’s concepts of autonomous and reconfigurable materials systems, like the Drosera capensis leaf, reconfigurable fiber networks that effectively concentrate particulates within a localized spot that can act as a detection patch are developed. These networks, prepared by electrohydrodynamic co‐jetting, draw their reconfigurability from a bicompartmental fiber architecture. Upon exposure to neutral pH, a differential swelling of both fiber compartments gives rise to interfacial tension and ultimately results in shape reconfiguration of the fiber network. Compared to free particles, the reconfigurable fiber networks display a 57‐fold increase in analyte detectability, average transport efficiencies of 91.9 ± 2.4%, and separation selectivity between different surface properties of 95 ± 3%. The integration of biomimetic materials into microanalytical systems, exemplified in this study, offers ample opportunities to design novel and effective detection schemes that circumvent mass transport limitations.Biomimetic hydrogel fibers deposited in a structured spiderweb network via electrohydrodynamic co‐jet writing allow for precise control over the direction of their bending motion. The shape reconfigurable network exhibits high selectivity and efficiency in actively transporting particulates. Based on these results, their potential in overcoming mass transport limitations in microanalytical systems is demonstrated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/174803/1/adfm202204080-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/174803/2/adfm202204080_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/174803/3/adfm202204080.pd