The Epilogue Problem: Termination-Aware Reflective Handoff in Large Language Model Agents

Large language model (LLM) agents increasingly rely on memory mechanisms to sustain continuity across long interactions, recurring tasks, and multi-session engagement. Recent work has advanced episodic memory, hierarchical memory architectures, memory operating systems, and governance mechanisms for controlling drift, corruption, and privacy risks in persistent agent memory. Yet one boundary condition remains insufficiently foregrounded: the termination of an individual runtime or session. In deployed systems, finite agent instances often end due to context-window saturation, orchestration resets, timeout policies, or infrastructure renewal. Existing approaches usually address this through transcript replay, summarization, or persistent memory writes. Still, they do not consistently treat termination itself as a privileged computational event for selective reflection and transfer.
We formalize this design gap as the epilogue problem: how should an LLM agent identify, compress, and transfer high-value session-specific patterns before a finite runtime ends? We propose an Epilogue Layer, an external middleware module that activates under likely termination, performs reflective consolidation over a structured session trace, applies governed filtering criteria, and produces two outputs: a human-facing epilogue and a machine-facing handoff object. Unlike conventional summarization, the proposed layer targets the preservation of transient but consequential interaction structure, including dominant themes, user-corrected assumptions, collaborative hypotheses, procedural preferences, and unresolved loops.