Cloud agents run each task in an isolated MicroVM. The trouble is the harness loop inside that guest: the harness is nearly idle while it waits on the model, then usage rises on a tool whose size is known only at run time, which complicates memory management from the host. Existing approaches infer reclaim targets from access frequency and memory footprint while the guest allocator places the idle harness and the short-lived tool on the same pages. Therefore, reclamation either selects the wrong pages, fixes on an incorrect capacity, or partially reclaims a huge page, splitting its transparent huge page. This paper proposes Tide, a proactive memory reclamation technique for agent MicroVMs. Our key insight is that the harness already knows each phase's memory intent (which memory, when, and whether its contents must survive), so the guest can report that intent as an exclusive guest-physical region for the host to reclaim directly. To realize this insight, we introduce an abstraction called arena and a set of user-space APIs for managing memory with exclusive intent; a guest-kernel allocator that groups exclusive allocations into huge-page-aligned extents; and a hypervisor extension that resolves those extents to host backing and applies the reported action, leaving guest capacity unchanged. Experiments on recorded agent trajectories show that Tide outperforms state-of-the-art mechanisms while incurring low overhead.

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