Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations
arXiv:2602.19320v2 Announce Type: replace-cross Abstract: Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows. Despite rapid architectural development, the empirical foundations of these systems remain fragile: existing benchmarks are often underscaled, evaluation metrics are misaligned with semantic utility, performance varies significantly across backbone models, and system-level costs are frequently overlooked. This survey presents a structured analysis
The rapid development and deployment of LLM agents make a structured analysis of their memory systems and limitations crucial for their reliable scaling and adoption.
A deeper understanding of agentic memory's current limitations is essential for guiding future research, development, and investment in autonomous AI systems, which are poised to transform numerous industries.
This analysis provides a foundational taxonomy and highlights empirical weaknesses in current agentic memory systems, shifting the focus towards more rigorous evaluation and systemic development.
- · AI researchers
- · Agentic AI developers
- · AI platform providers
- · Overhyped AI agent startups
- · Inadequately tested AI agent deployments
The findings will lead to a re-evaluation of existing agentic memory architectures and benchmark standards.
Improved memory systems will enable more robust and capable LLM agents, accelerating their integration into complex workflows.
The enhanced reliability of AI agents could lead to significant collapse of white-collar workflows and the emergence of new service models.
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Read at arXiv cs.AI