The learning approach, understood as the process through which agribusiness creates knowledge and develops capabilities, is key to understanding the voluntary effort made by the firm to acquire the capabilities necessary to compete in an agricultural innovation system (AIS) and improve their transition to sustainability. In this framework, learning is understood as a complex phenomenon emerging alongside specialization. Agent-based modelling (ABM) has proven to be an appropriate method of analysis for such phenomena; however, existing models have limitations related to the bounded rationality of agents, their relational proximity, and market forces. In order to help overcome these limitations, we propose this model representing the local dynamics of competing and collaborating innovation agents, and the complementarity of their capabilities. The model makes it possible to study the dynamics of local learning and how patterns of specialization emerge, and to improve the transfer and adoption of technologies (smart farming), increasing their productivity and sustainability, and reducing their environmental impact in an agricultural innovation system. It also provides a point of reference to guide policies, programs, and strategies aiming to improve the system’s economic and innovative performance. To achieve this objective, we use a case study of the banana production chain to build an agent-based model.