Bringing Attention To Memory In AI Agents and Agentic Systems
FAQs
Memory type is a specialized cognitive function within an AI agent's memory, much like how human memory has different systems for different purposes. A memory type defines both the temporal characteristics (how long information lasts) and functional characteristics (what specific purpose it serves) of stored information within an agent's memory architecture.
Common high-level memory types are short-term and long-term memory, which have distinct memory types within them.
Short-term memory types handle immediate, temporary information needs. Working Memory acts as the agent's "scratchpad" for active information manipulation during current tasks - imagine it as the mental workspace where an agent holds relevant details while solving a problem. Semantic Cache stores recent query-response pairs, enabling instant retrieval of similar requests without reprocessing - like having quick answers ready for frequently asked questions. Shared Memory enables coordination between multiple agents by providing a collaborative workspace where different agents can access and contribute information simultaneously.
Long-term memory types provide the foundation for persistent learning and adaptation. Episodic Memory maintains records of specific events and interactions, similar to your autobiographical memories of particular conversations or experiences. Semantic Memory serves as the agent's organized knowledge repository about facts, concepts, and relationships - think of it as a well-structured library of world knowledge. Procedural Memory stores workflows and skills, enabling agents to execute complex multi-step processes automatically, much like how you can ride a bicycle without consciously thinking about each movement. Finally, Associative Memory creates and maintains relationships between different pieces of information, allowing agents to "connect the dots" and make inferences by traversing these connections.
An application mode represents the fundamental operational pattern that characterizes how an AI agent interacts with its environment and users. Think of it as the agent's primary "way of being" - the behavioral framework that shapes how it approaches problems, manages information, and delivers value.
There are three core application modes based on extensive observation of real-world implementations. Assistant Mode focuses on conversational, task-oriented interactions where agents provide specialized help while building and maintaining relationships with users over time. This mode emphasizes personality consistency and adaptive responses based on evolving user needs. Workflow Mode centers on orchestrating complex, multi-step processes with systematic coordination and state management - imagine an agent that can manage entire business procedures from start to finish. Deep Research Mode involves comprehensive, multi-source analysis over extended periods, requiring progressive knowledge building and synthesis capabilities.
Understanding application modes helps you choose the right memory architecture for your specific use case, since each mode demands different combinations of memory types and optimization strategies.
Agent Memory (and Memory Management) is a computational exocortex for AI agents—a dynamic, systematic process that integrates an agent’s LLM memory (context window and parametric weights) with a persistent memory management system to encode, store, retrieve, and synthesize experiences. Inspired by human cognitive memory, it enables agents to accumulate knowledge, maintain conversational and task continuity, and adapt behavior based on history—making them more reliable, believable, and capable.
Memory units are the fundamental building block of agent memory - the smallest discrete piece of information that has been transformed from raw data into actionable memory. A memory unit is a structured container that holds not just information, but also the metadata and relationships that make that information useful for agent reasoning. Unlike simple data records, memory units carry attributes like temporal context (when the information was learned), strength indicators (how relevant or reliable the information is), associative links (how it connects to other memories), and retrieval metadata (how and when it should be accessed).
LLM memory exists within the language model itself and operates in two distinct layers. Parametric Memory represents the knowledge encoded in the model's neural network weights during training phases - this is the "baked-in" knowledge that remains static during inference but represents the model's core understanding of language, facts, and patterns learned from training data. Context window memory refers to the transient information the model can access during a single session within its context window limits - essentially the "working memory" of the current conversation that disappears when the session ends or when older information gets pushed out due to token limits.
Agent Memory is a combination of both the LLM memory and an external persistence management system that provides a computational exocortex that enables true continuity and learning within AI Agents. While the LLM serves as the cognitive engine processing information and generating responses, Agent Memory acts as the persistent substrate that accumulates knowledge, maintains context across sessions, and enables behavioral adaptation based on historical patterns.
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