The study of eco-evolutionary dynamics, i.e. of the inter-twinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco-evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. This presentation aims to put forward models that simulate interindividual interactions in an eco-evolutionary framework: the demo-genetic agent-based models (DG-ABMs). Being demo-genetic, DG-ABMs consider the feedback loop between ecological and evolutionary processes. Being agent-based, DG-ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. The ability of DG-ABMs to take into account the genetic heterogeneity - that affects individual decisions/traits related to local and instantaneous conditions - differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco-evolutionary feedback loops. This presentation relies first on the review study of Lamarins et al. (2021), and also on two case studies using DG-ABMs to investigate (i) the effect of connectivity and diversity on metapopulation eco-evolutionary dynamics, stability and adaptation in Atlantic salmon (Lamarins et al. 2022, 2023) and (ii) the adaptive capacity of forest stands in face of climate change, and the effect of silviculture on this adaptive capacity (Godineau, Fririon et al. 2023). These different examples illustrate that DG-ABMs explicitly or implicitly accounting for competitive, cooperative, or reproductive interactions are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organisation. By jointly modelling the effects of management practices and other eco-evolutionary processes on interindividual interactions and population dynamics, DG-ABMs are also effective prospective and decision support tools to evaluate the short- and long-term evolutionary costs and benefits of management strategies and to assess potential trade-offs. Godineau, C., Fririon, V., Beudez, N., de Coligny, F., Courbet, F., Ligot, G., … Lefèvre, F. (2023). A demo-genetic model shows how silviculture reduces natural density-dependent selection in tree populations. Evolutionary Applications, (March), 1–15. doi: 10.1111/eva.13606 Lamarins, A., Fririon, V., Folio, D., Vernier, C., Daupagne, L., Labonne, J., … Oddou-Muratorio, S. (2022). Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models. Evolutionary Applications, 15(12), 1988–2001. doi: 10.1111/eva.13508 Lamarins, A., Hugon, F., Piou, C., Papaïx, J., Prévost, E., Carlson, S. M., & Buoro, M. (2022). Implications of dispersal in Atlantic salmon: lessons from a demo-genetic agent-based model. Canadian Journal of Fisheries and Aquatic Sciences, 79(12), 2025–2042. doi: 10.1139/cjfas-2021-0342 Lamarins, A., Prévost, E., Carlson, S. M., & Buoro, M. (2024). The importance of network spatial structure as a driver of eco-evolutionary dynamics. Ecography, 2024(1), 1–13. doi: 10.1111/ecog.06933