A major, ongoing social transition is the inclusion of autonomous agents into human organizations. For example, in defence and security applications, robots may be used alongside human operatives to reduce risk or add capability. But a key barrier to the transition to successful human-autonomous agent collectives is the need for sufficient trust between team members. A critical enabling factor for this trust will be a suitably designed dynamic allocation of function (AoF). We consider AoF in terms of a 'ladder of trust' (from low to high) with individual team members adjusting trust in their teammates based on variation in 'score' over time. The score is derived by the ability of team member to perceive and understand its situation based on the gathered information and act to acheive team or self goals. Combining these trust scores gives a system-level perspective on how AoF might be adjusted during a mission. That is, the most suitable teammate for a function might have a low trust rating from its fellow teammates, so it might be preferable to choose the next most suitable teammate for the function at that point in time. Of course, this is only in the situation where the next most suitable teammate is also likely to perform within the set framework of moral, ethical, and legal constraints. The trade-offs between trust in the individual agent's capability and predictability need to be considered within the broader context of the agent's integrity and accountability. From this perspective, the Allocation Space is defined by more than ability of each agent to perform a function.