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.

• The paper introduces a hybrid memory architecture combining working, episodic, and semantic memory to mitigate memory inflation and contextual degradation.
• It demonstrates that intelligent decay and a user-centric interface improve task completion and cost efficiency in LCNC agents.
• Experimental evaluation reveals higher consistency and reduced token cost compared to traditional sliding windows and RAG methods.

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

Introduction

The paper "Memory Management and Contextual Consistency for Long-Running Low-Code Agents" addresses the critical challenges of memory management within Low-Code/No-Code (LCNC) platforms. With the growing deployment of LCNC platforms, autonomous AI agents undertake complex tasks for extended periods, such as customer service automation and supply chain management. However, these agents encounter severe limitations due to "memory inflation" and "contextual degradation," which diminish their effectiveness and increase computational costs over time.

Contextual Challenges

The finite context window of LLMs poses significant challenges in long-duration processes. As memory grows in size, older observations are lost, resulting in poor task coherence and increased error propagation, termed "self-degradation." This paper identifies the need for a more sophisticated memory management system that can effectively address these problems by ensuring both cost efficiency and context consistency while being accessible for non-technical users.

Hybrid Memory Architecture

The proposed solution is a hybrid memory architecture that integrates components of episodic and semantic memory, inspired by cognitive science. The architecture consists of:

1. Working Memory (WM): Manages the immediate context within the agent's short-term operations.
2. Episodic Memory (EM): Stores detailed, time-indexed events for each interaction.
3. Semantic Memory (SM): Retains general knowledge and summarized information from episodic memory.

This framework emulates active forgetting, where unimportant memories are pruned to create a leaner, more relevant memory repository. This process, termed "Intelligent Decay," relies on a composite utility score to assess memory relevance based on recency, relevance, and user-specified utility.

Intelligent Decay Mechanism

The Intelligent Decay mechanism ensures that the episodic memory is organized and compact, preventing unnecessary data accumulation. The utility of each memory entry is calculated using a composite score of recency, relevance, and user utility. Low-score memories are candidates for removal or consolidation into the semantic memory, through which high-value information is distilled.

User-Centric Interface

A key feature of the system is a user-centric visualization interface that enables non-technical managers to directly interact with and influence the agent's memory. Users can mark significant interactions to retain or discard, aligning with Human-in-the-Loop (HITL) frameworks to enhance AI transparency and control.

Experimental Evaluation

Experiments demonstrate the superiority of the hybrid memory system over traditional approaches like sliding windows and Retrieval-Augmented Generation (RAG). The hybrid system significantly improves task completion rates and consistency metrics while reducing the average token cost and contradiction rates during long-running operations. These improvements underline the potential of active, efficient memory management over passive methods.

Conclusion

The paper presents a robust solution to the persistent memory challenges in LCNC agents. By embedding cognitive strategies into the hybrid memory architecture, combined with an interactive interface for non-technical users, the system offers a balance of performance improvement and user manageability. Although further work is needed to optimize parameter tuning and increase automation, the study establishes a foundational framework for future developments in long-term AI learning and adaptability in low-code environments. The approach not only enhances existing LCNC systems but also aligns with broader AI trends towards user empowerment and responsible AI deployment.