Research actively explores and advances the play strength of general agents, which are able to play video games without having specific knowledge about them. However, how general agents impact player experience and motivation when implemented in commercially viable games is largely unexplored. In this paper, we investigate this relationship as initial work towards linking general agent behaviour and player experience as a step towards making general agents applicable to commercial video games. Specifically, we created two versions of a simple competitive human-versus-agent game having two general Monte Carlo Tree Search (MCTS) agents with different behaviours. These agents, without having specific knowledge about the game, have two unique goals: i) maximising score; and ii) exploring (more suitable for the game we chose). We integrated these agents into a ’capture the flag’ game and conducted a study to investigate the effects on several player motivation components of the Intrinsic Motivation Inventory (IMI) and Player Experience of Need Satisfaction (PENS) scale. Enquiry in this direction opens up the possibilities to start analysing general agents from the perspective of the player’s journey.