Research

Cognitive continuity describes an AI agent's ability to preserve, retrieve, update and evolve the internal structures that influence its behavior across interactions and over time.

Persistent AI agents preserve behaviorally relevant internal state across interactions, allowing memory, knowledge, goals and other durable structures to influence future behavior.

Agent memory is the set of mechanisms through which an AI agent retains, retrieves, updates, organizes and uses information from previous states or experiences to influence current and future behavior.

Episodic memory in an AI agent represents specific experiences or events with enough contextual information for those past episodes to be retrieved and used in later reasoning or behavior.

Semantic memory in an AI agent represents generalized knowledge that can persist beyond individual experiences and be retrieved to support future reasoning, decisions and behavior.

Memory consolidation in AI agents is the process of transforming accumulated memories and experiences into more stable, compact and reusable representations that can influence future reasoning and behavior.

Memory retrieval in AI agents is the process of selecting previously stored memories that are relevant to the agent's current context, goals, reasoning and decisions.

Working memory in AI agents is the limited, transient set of information currently available to active reasoning, planning, decision-making and behavior.

Memory forgetting in AI agents is the controlled reduction of a memory's retention, accessibility or cognitive influence when that information becomes obsolete, redundant, low-value, unsafe or no longer relevant.

Attention in AI agents is the mechanism that prioritizes which observations, memories, goals and active states receive cognitive processing at a given moment.

Goal management in AI agents is the process of representing, prioritizing, activating, monitoring, reconsidering and completing desired states across cognitive cycles and execution boundaries.

Planning in AI agents is the process of constructing and maintaining candidate sequences, structures or strategies that can move the agent from its current state toward an active goal.

Reasoning is the cognitive process through which an AI agent transforms context, evidence, memory, knowledge, goals, and internal state into structured inferences that can inform decisions, planning, and behavior.

Decision making in AI agents is the cognitive process of evaluating alternatives and committing to a selected course based on reasoning, goals, intentions, drives, constraints, evidence, and uncertainty.

Behavior generation is the cognitive process through which an AI agent transforms its current cognitive state, decisions, plans, context, and learned signals into candidate behaviors that can be evaluated and selected before execution.

Action execution is the governed transition through which an AI agent attempts to transform an operational intention into an effect while preserving authorization, failure, cancellation, result, and observability boundaries.