The benefits of programmatic assessment are well-established. Evidence from multiple assessment formats is accumulated and triangulated to inform progression committee decisions. Committees are consistently challenged to ensure consistency and fairness in programmatic deliberations. Traditional statistical and psychometric techniques are not well-suited to aggregating different assessment formats accumulated over time. Some of the strengths of programmatic assessment are also vulnerabilities viewed through this lens. While emphasis is often placed on data richness and considered input of qualified experts, committees reasonably wish for practical, defensible solutions to these challenges. We draw upon on existing literature regarding Bayesian Networks (BN), noting their utility and application in educational systems. We provide illustrative examples of how they could potentially be used in contexts that embed programmatic principles. We show a simple BN for a knowledge domain before presenting a full-scale 'proof of concept' BN to support committee decisions. We zoom in on one 'node' to demonstrate the capacity of incorporating disparate evidence throughout the network.