LLM-based agents increasingly tackle long-horizon tasks with interdependent decisions, where each action reshapes future constraints and intermediate errors can cascade. Existing RAG and agent memory systems organize histories by semantic similarity, retrieving content-relevant entries at decision time. We argue that this design mismatches execution-state dependencies: it fragments decision trajectories and mixes valid and erroneous traces, hindering coherent state reconstruction and error isolation. We propose MAGE (**M**emory as **A**gent-**G**uided **E**xploration), an active execution-state manager that stores interactions in a hierarchical state tree. The agent derives its state from the active root-to-current path, combining subgoal summaries, recent traces, and hints from prior branches. Four coupled operations maintain the tree: *Grow* records new traces, *Compress* summarizes completed subgoals, *Maintain* validates summaries, and *Revise* restores a target boundary and resumes on a new branch. This design bounds context growth while preserving state integrity and isolating flawed segments from the active path. Experiments on MemoryArena show that MAGE improves the average task success rate by 7.8–20.4 pp over baselines, while reducing token consumption by 55.1%. Code is available at: https://github.com/Komorebi660/MAGE.

While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prospective memory (PM), the ability to spontaneously recall and act on latent constraints without direct requests, remains largely unevaluated. We introduce **TriggerBench**, a five-dimensional PM benchmark across daily assistants and professional workflows. TriggerBench pairs scenarios with matched RM controls, contrastive positive/negative variants, and overloaded triggers, enabling fine-grained measurement of proactive recall, false-alarm rate, and attentional robustness. We find: (i) PM shows a precision-recall trade-off and attentional fragility. Though enhanced reasoning improves proactive recall, models can overfit to always-remind heuristics. Furthermore, PM accuracy degrades substantially under implicit constraints or triggers overloaded by concurrent requests, indicating that robust PM remains an open challenge. (ii) PM is notably harder than RM: on identical contexts, RM remains near-saturated up to 100K tokens, while PM decays sharply as context length increases. (iii) PM may serve as a behavioral probe of spare reasoning capacity. Pairing PM with AIME-2025 math problems reveals that successful trajectories yield higher PM accuracy than failed ones at the same context length, showing PM is a behavioral correlate of spare reasoning budget that token count obscures. Project page: https://github.com/KristenZHANG/TriggerBench-Official.