COMPEL Glossary / persistent-memory
Persistent Memory
Persistent memory extends an AI agent's learning beyond a single session by storing information -- facts, preferences, outcomes, strategies -- in an external memory system that is retrieved when processing new tasks.
What this means in practice
Memory types include conversation memory (summaries of past interactions), episodic memory (records of specific experiences), semantic memory (factual knowledge), and procedural memory (learned strategies). Persistent memory fundamentally changes governance because the agent's behavior becomes a function of accumulated experience, not just its training and instructions. Governance challenges include memory drift (behavior diverging from design intent), memory poisoning (adversaries corrupting stored memories), stale memory (outdated strategies applied to changed contexts), privacy compliance (memories containing personal data subject to deletion rights), and reproducibility (two instances with different memories behave differently).
Why it matters
Persistent memory fundamentally changes AI governance because agent behavior becomes a function of accumulated experience, not just training and instructions. This creates novel risks including memory drift, memory poisoning, stale strategies, and privacy compliance challenges. Organizations deploying agents with persistent memory face governance complexity that traditional AI oversight frameworks were not designed to address.
How COMPEL uses it
COMPEL's Agent Governance cross-cutting layer addresses persistent memory risks through classification and monitoring requirements that escalate with agent autonomy level. During the Model stage, memory architecture decisions are part of the Technology pillar design. The Evaluate stage includes memory audit procedures to detect drift and poisoning, with privacy compliance requirements tracked through the Governance pillar.
Related Terms
Other glossary terms mentioned in this entry's definition and context.