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

The paper addresses the critical challenge of maintaining coherent, efficient memory in long-running LCNC agents powered by LLMs. As interaction history grows, agents encounter memory inflation (context bloat and token cost) and contextual degradation (loss of important prior information), which cause stateless and incoherent behavior. The research question is how to design a memory system that preserves relevant long-term context, mitigates error propagation, and remains transparent and manageable for non-technical LCNC users. The proposed solution is a hybrid memory architecture featuring distinct episodic and semantic stores coupled with an Intelligent Decay mechanism and a human-in-the-loop (HITL) interface, aiming to improve robustness, cost-efficiency, consistency, and user control.

Foundational strategies include sliding window truncation (keeping recent N turns), message summarization (condensing older dialogue into summaries appended to prompts), and retrieval-augmented generation (RAG) using external knowledge bases. These approaches mitigate context limits but risk losing long-term context or depend heavily on retrieval quality. Advanced memory architectures like MIRIX (multi-component memory including episodic, semantic, procedural) and A-MEM (Zettelkasten-inspired interconnected knowledge network) offer structured, dynamic memory but are complex and lack accessible interfaces for non-technical users. Cognitive science distinguishes episodic versus semantic memory and highlights active forgetting to prevent catastrophic interference, informing the paper’s Intelligent Decay design.

The proposed hybrid memory system comprises: (1) Working Memory (WM): the immediate LLM context window for ongoing interaction; (2) Episodic Memory (EM): a vector database of fine-grained, time-indexed MemoryEntry objects (content, timestamp, embedding) enabling semantic retrieval and hindsight; and (3) Semantic Memory (SM): a compact long-term knowledge base (facts, summaries, knowledge graph) distilled from episodic experiences. The Intelligent Decay mechanism assigns each episodic entry a Utility Score S(M_i) = αR_i + βE_i + γU_i, where R_i is an exponential time-decay recency term, E_i is cosine similarity to the current task vector, and U_i is a discrete human-assigned utility. Entries below a threshold are pruned or consolidated: low-utility memories can be summarized via an LLM into factual abstractions stored in SM, preserving core knowledge while reducing EM size. Algorithmically, the system periodically computes S(M_i) for all entries, flags low-scoring items for deletion or consolidation, and updates stores accordingly. A user-centric timeline interface provides transparency and control: visual decay indicators for each interaction node and simple actions—Retain (pin), Forget (strike-through), and Consolidate (abstract)—which directly adjust U_i or trigger distillation. Experiments simulate a 500-turn LCNC project-planning task comparing three strategies (10-turn sliding window, basic RAG without decay, and the full hybrid system). Evaluation metrics include task completion, latency, token cost, consistency via LLM-as-a-judge (semantic similarity and contradiction rate), and memory metrics (store size, retrieval latency, decay efficiency).

In simulated long-running tasks (500 turns), the hybrid system outperforms baselines: Task Completion Rate 92.5% vs. 81.4% (Basic RAG) and 65.2% (Sliding Window); Average Token Cost per turn 890 vs. 1150 (Basic RAG) and 580 (Sliding Window); Latency 200 ms vs. 250 ms (Basic RAG) and 120 ms (Sliding Window). Consistency metrics: Semantic Consistency Score 0.94 vs. 0.89 (Basic RAG) and 0.78 (Sliding Window); Contradiction Rate 1.2% vs. 5.5% (Basic RAG) and 18.1% (Sliding Window). Qualitatively, the hybrid system prevents self-degradation associated with indiscriminate memory accumulation, exhibiting slight performance improvements over time (self-evolution) due to proactive decay and HITL corrections.

The findings demonstrate that proactive, cognitively inspired memory management with user oversight addresses memory inflation and contextual degradation in LCNC agents. By scoring and curating episodic memories, distilling salient knowledge into semantic memory, and enabling users to pin or discard facts, the system maintains long-term coherence and reduces contradictions. This leads to higher task completion and better cost-performance balance compared to sliding windows and basic RAG. The results substantiate the hypothesis that active forgetting and structured consolidation mitigate error propagation and catastrophic interference, providing a practical, transparent framework aligned with LCNC user needs.

The paper introduces a hybrid memory architecture with an Intelligent Decay mechanism and a user-centric interface for LCNC agents, solving key long-duration challenges of memory inflation and contextual degradation. Empirical simulations show improved task completion, reduced contradictions, and enhanced long-term consistency versus common baselines. Future work includes autonomous calibration of decay parameters (learning α, β, γ), structured pruning for efficiency, multimodal extensions, adding procedural memory, integration with stateful agent frameworks (e.g., LangGraph-like patterns), and advances in optimization and domain-specific fine-tuning to further improve efficiency and expertise.

The Intelligent Decay mechanism requires careful tuning of α, β, γ to balance recency, relevance, and user utility, which may be task-specific. The effectiveness of the user-centric interface depends on consistent, high-quality HITL feedback, introducing a human bottleneck and variability. Practical deployments must account for user engagement and calibration overhead; generalization beyond simulated tasks and reliance on embedding/task vector quality also warrant further study.