10 Comprehensive Resources about Agentic Memory

2026-05-10 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The provided content explores the concept of "agentic memory" for AI systems, presenting a collection of ten resources including surveys, research papers, and interviews. These resources delve into how AI agents utilize memory for complex interactions and real-world tasks, categorizing memory types, architectures, and evaluation methods. Key topics include security risks in long-term memory, graph-based memory for self-evolving agents, practical system limitations, and the comparison of AI memory to human memory, including multimodal aspects. An interview with Shawn Shen, co-founder of Memories AI, highlights the need for long-term visual memory in embodied AI, discussing their Large Visual Memory Model based on Transformer architecture, designed for encoding and retrieval rather than creativity, and its application in devices like smart glasses and robotics.

Key takeaway

For CTOs and VPs of Engineering building embodied AI or agentic systems, prioritize developing robust, on-device visual memory solutions. Your focus should be on efficient encoding and retrieval mechanisms, rather than creative generation, to enable long-term contextual awareness and object permanence. This approach will reduce bandwidth burdens and enhance personalization, making AI agents truly effective in real-world applications like robotics and smart wearables.

Key insights

Agentic memory is crucial for AI agents to handle complex, real-world tasks and enable embodied intelligence.

Principles

• AI intelligence requires separate memory systems.
• Memory systems should prioritize encoding and retrieval.
• On-device processing is critical for embodied AI.

Method

Memories AI trains a Large Visual Memory Model using Transformer architecture for encoding multimodal data into embeddings, focusing on lucid reconstruction rather than creativity, and developing a robust retrieval system.

In practice

• Implement visual memory for embodied AI applications.
• Design memory systems for on-device, low-power operation.
• Focus on encoding for machine, not human, for efficiency.

Topics

• Agentic Memory
• Embodied AI
• Visual Memory Models
• On-device AI
• Memory Security

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.