How AI Agents Remember: The Evolution of Agentic Memory (2025 Guide)

Memory is the cornerstone capability that transforms static Large Language Models (LLMs) into adaptive AI agents capable of long-horizon reasoning and continual evolution. In this video, we dive deep into the comprehensive survey, "Memory in the Age of AI Agents," to explore how the next generation of artificial intelligence builds, maintains, and utilizes its own "brain".
What You Will Learn:
• The 3 Forms of Memory (The "How"): We break down the architectural realizations of memory, including Token-level (explicit, discrete units), Parametric (internalized within model weights), and Latent (implicit internal representations like KV caches).
• The 3 Functional Pillars (The "Why"): Memory isn't just one thing. We look at Factual Memory for consistency, Experiential Memory for skill acquisition and self-evolution, and Working Memory for active, transient context management.
• The Operational Dynamics: How agents perform memory Formation (distilling raw data), Evolution (consolidating insights and "forgetting" low-value data), and Retrieval (context-aware queries).
• The RL Frontier: Why the field is moving away from hand-crafted heuristic pipelines toward fully Reinforcement Learning (RL)-driven memory systems where agents autonomously learn how to manage their own minds.
Why This Matters: Traditional taxonomies like long/short-term memory are no longer enough. Understanding these new Forms, Functions, and Dynamics is essential for anyone building or researching the future of AGI and agentic intelligence.
Timestamps: 0:00 - The Paradigm Shift: From LLMs to Agents 2:15 - Memory Forms: Token, Parametric, & Latent 5:40 - Memory Functions: Factual vs. Experiential vs. Working 9:10 - Lifecycle Dynamics: How Agents Form & Refine Memories 12:30 - The Future: RL-Driven & Multimodal Memory
Reference Source: "Memory in the Age of AI Agents: A Survey" (arXiv:2512.13564)
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