AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks

Олег Ивченко; Iryna Ivchenko

doi:10.5281/zenodo.21927313

August 14, 2026Open Access

AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks

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Key Points

To examine how integrating episodic, semantic, and working memory architectures improves context retention, decision-making, and execution in AI agents handling complex, long-horizon tasks.
Analyzed multi-tiered cognitive memory frameworks combining short-term working context with long-term episodic recall and semantic knowledge retrieval.
Evaluated agent execution across multi-step environments requiring sustained state tracking, tool usage, and cross-temporal reasoning.
Hierarchical memory integration reduces context window degradation and state-tracking errors during extended multi-step task execution compared to single-context baseline agents.
Dual retrieval of episodic experiences and structured semantic facts improves agent adaptability and consistency across long-horizon planning sequences.

Implication

Computational review demonstrates how combining episodic, semantic, and working memory enhances AI agent planning across long-horizon tasks, indicating a path toward persistent autonomous systems.

Abstract

Research article: AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks

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Cite This Study

Ивченко et al. (2026) studied this question.

synapsesocial.com/papers/6a817a21f2fb91fc834adb77 https://doi.org/10.5281/zenodo.21927313

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AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks

Key Points

Implication

Abstract

Discussion

Cite This Study

AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks

Key Points

Implication

Abstract

Discussion

Cite This Study

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