This paper characterizes the fundamental limits of integrated sensing and communication (ISAC) when the transmitter is subject to a representation-constrained encoder modeled via a mutual information (MI) bottleneck and the receiver employs a fluid antenna system (FAS). Specifically, the message is first encoded into an ideal Gaussian waveform and then mapped by a stochastic encoder into a finite capacity representation that serves as the channel input in the adopted analytical abstraction, while the FAS receiver selects the port with the most favorable channel conditions. From an information-theoretic perspective, we show that the representation bottleneck is equivalent to an additive representation noise, which reduces both the communication and sensing signal-to-noise ratios (SNRs) at the selected port. We then derive the resulting ISAC capacity-distortion region and establish tight converse and achievability bounds under general fading models, including Jakes correlated channels. Leveraging the spatial degrees of freedom (DoF) characterization of the Jakes' model, we prove that the port selection gain is fundamentally constrained by the physical length of the FAS region: the effective diversity order equals the numerical rank of the Jakes' correlation matrix and increases only with the FAS length. Consequently, enlarging the FAS length allows the selected communication SNR to approach the representation-limited ceiling, driving the achievable rate toward its fundamental bound. The sensing mean square error (MSE) similarly approaches its bound when the selected sensing gain also increases with the FAS aperture. Numerical results corroborate the analysis and scaling laws.

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