Agricultural innovation systems (AIS) are increasingly recognized as complex adaptive systems in which interventions cannot be expected to create predictable, linear impacts. Nevertheless, the logic models and theory of change (ToC) used by standard-setting international agricultural research agencies and donors assume that agricultural research will create impact through a predictable linear adoption pathway which largely ignores the complexity dynamics of AIS, and which misses important alternate pathways through which agricultural research can improve system performance and generate sustainable development impact. Despite a growing body of literature calling for more dynamic, flexible and “complexity-aware” approaches to monitoring and evaluation, few concrete examples exist of ToC that takes complexity dynamics within AIS into account, or provide guidance on how such theories could be developed. This paper addresses this gap by presenting an example of how an empirically-grounded, complexity-aware ToC can be developed and what such a model might look like in the context of a particular type of program intervention. Two detailed case studies are presented from an agricultural research program which was explicitly seeking to work in a “complexity-aware” way within aquatic agricultural systems in Zambia and the Philippines. Through an analysis of the outcomes of these interventions, the pathways through which they began to produce impacts, and the causal factors at play, we derive a “complexity-aware” ToC to model how the cases worked. This middle-range model, as well as an overarching model that we derive from it, offer an alternate narrative of how development change can be produced in agricultural systems, one which aligns with insights from complexity science and which, we argue, more closely represents the ways in which many research for development interventions work in practice. The nested ToC offers a starting point for asking a different set of evaluation and research questions which may be more relevant to participatory research efforts working from within a complexity-aware, agricultural innovation systems perspective.