{"ID":2864618,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25250","arxiv_id":"2509.25250","title":"Memory Management and Contextual Consistency for Long-Running Low-Code Agents","abstract":"The rise of AI-native Low-Code/No-Code (LCNC) platforms enables autonomous agents capable of executing complex, long-duration business processes. However, a fundamental challenge remains: memory management. As agents operate over extended periods, they face \"memory inflation\" and \"contextual degradation\" issues, leading to inconsistent behavior, error accumulation, and increased computational cost. This paper proposes a novel hybrid memory system designed specifically for LCNC agents. Inspired by cognitive science, our architecture combines episodic and semantic memory components with a proactive \"Intelligent Decay\" mechanism. This mechanism intelligently prunes or consolidates memories based on a composite score factoring in recency, relevance, and user-specified utility. A key innovation is a user-centric visualization interface, aligned with the LCNC paradigm, which allows non-technical users to manage the agent's memory directly, for instance, by visually tagging which facts should be retained or forgotten. Through simulated long-running task experiments, we demonstrate that our system significantly outperforms traditional approaches like sliding windows and basic RAG, yielding superior task completion rates, contextual consistency, and long-term token cost efficiency. Our findings establish a new framework for building reliable, transparent AI agents capable of effective long-term learning and adaptation.","short_abstract":"The rise of AI-native Low-Code/No-Code (LCNC) platforms enables autonomous agents capable of executing complex, long-duration business processes. However, a fundamental challenge remains: memory management. As agents operate over extended periods, they face \"memory inflation\" and \"contextual degradation\" issues, leadin...","url_abs":"https://arxiv.org/abs/2509.25250","url_pdf":"https://arxiv.org/pdf/2509.25250v1","authors":"[\"Jiexi Xu\"]","published":"2025-09-27T08:01:26Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.SE\"]","methods":"[]","has_code":false}