Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Memory Management and Contextual Consistency for Long-Running Low-Code Agents

Jiexi Xu

TL;DR

This paper addresses memory management for long-running LCNC agents, identifying memory inflation and contextual degradation as key obstacles to reliable, cost-efficient operation. It introduces a cognitively inspired hybrid memory system with episodic and semantic stores, coupled with an Intelligent Decay mechanism that computes a composite utility $S(M_i)$ to prune or consolidate memories. A HITL-enabled visualization interface enables non-technical users to influence memory via simple actions, enhancing trust and control. Across simulated long-running tasks, the proposed approach outperforms sliding-window and basic RAG baselines in task completion, consistency, and token-cost efficiency, validating a shift toward proactive, transparent memory management for enterprise AI 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.

Memory Management and Contextual Consistency for Long-Running Low-Code Agents

TL;DR

This paper addresses memory management for long-running LCNC agents, identifying memory inflation and contextual degradation as key obstacles to reliable, cost-efficient operation. It introduces a cognitively inspired hybrid memory system with episodic and semantic stores, coupled with an Intelligent Decay mechanism that computes a composite utility to prune or consolidate memories. A HITL-enabled visualization interface enables non-technical users to influence memory via simple actions, enhancing trust and control. Across simulated long-running tasks, the proposed approach outperforms sliding-window and basic RAG baselines in task completion, consistency, and token-cost efficiency, validating a shift toward proactive, transparent memory management for enterprise AI 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.

Paper Structure

This paper contains 25 sections, 1 equation, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Paradigm Shift of LCNC Agents
  3. The Contextual Degradation Problem
  4. Our Contribution
  5. Related Work
  6. Foundational Memory Strategies
  7. Advanced Memory Architectures
  8. Cognitive Models of Memory and Forgetting
  9. Hybrid Memory System for LCNC Agents
  10. System Architecture
  11. Intelligent Decay Mechanism
  12. Mathematical Formulation
  13. Algorithm and Pseudo-Code
  14. Smart Summarization and Consolidation
  15. User-Centric Visual Memory Management Interface
  16. ...and 10 more sections

Figures (2)

  • Figure 1: Hybrid Memory System Architecture Diagram. Arrows indicate information flow. Solid lines denote primary flow, and dashed lines denote secondary or on-demand processes.
  • Figure 2: Visualization of the impact of different memory strategies on long-term agent performance, illustrating the "self-degradation" problem and the advantage of our "self-evolution" approach.