Glossary
Memory Stack
A memory stack is the complete set of persistent information storage systems that an AI agent uses to retain context, learned information, and interaction history across sessions.
What is Memory Stack?
An AI agent's memory stack determines what it remembers between interactions and how that memory influences its future behavior. Unlike the ephemeral context of a single conversation, the memory stack provides persistent storage that shapes the agent over time. This can include conversation histories, learned user preferences, accumulated knowledge, task outcomes, and any other information the agent retains.
Memory stacks vary widely in architecture. Some agents use simple conversation buffers that store the last N interactions. Others employ sophisticated vector databases that enable semantic search over thousands of past interactions. Some maintain structured knowledge graphs, and others use hybrid approaches combining multiple memory types. The architecture of the memory stack significantly affects agent behavior, capability, and risk profile.
From a trust perspective, the memory stack is both an asset and a liability. Rich memory enables agents to provide more personalized, context-aware service, improving quality and reliability. But memory also creates risks: stored personal data creates privacy exposure, outdated information can lead to errors, and memory manipulation could alter agent behavior in unintended ways. Trust scoring must account for both the benefits and risks of an agent's memory architecture.
Example
A customer success agent's memory stack includes three layers: (1) a conversation buffer storing the last 30 days of interactions with each customer, (2) a vector database of 10,000 support resolution records for semantic search, and (3) a structured preference store tracking each customer's communication preferences and product configurations. When a customer contacts the agent, all three memory layers inform the response, enabling personalized support without the customer having to repeat context.
How Signet addresses this
Signet includes the memory stack as a component of the configuration fingerprint. Changes to the memory architecture trigger a 5% score decay, reflecting the moderate impact that memory changes have on agent behavior. Signet also evaluates memory-related practices as part of the Security dimension, including data retention policies, access controls, and compliance with data protection regulations.
Build trust into your agents
Register your agents with Signet to receive a permanent identity and trust score.