Behavior Generation
Behavior generation is the process through which an AI agent converts its current cognitive state into candidate behaviors that can be ranked and selected before execution.
Behavior generation in an AI agent is the cognitive process through which the agent transforms its current cognitive state, commitments, plans, context, and learned signals into candidate behaviors representing possible ways of responding or progressing before operational selection and execution.
In short
- →Behavior generation creates candidate behaviors rather than executing actions.
- →Generation is distinct from ranking and selection.
- →Decision making can influence behavior without directly executing it.
- →Planning can guide behaviors associated with current plan progress.
- →Learning can influence later behavioral ranking.
- →A selected behavior can still be rejected or fail at the execution boundary.
Definition
Behavior generation produces explicit candidate behaviors from the agent's current cognitive state.
Those candidates can later be ranked, selected, checked for actionability, and translated into execution.
Behavior vs. Action
Behavior represents a possible or selected operational direction. Action represents execution.
A behavior therefore does not imply that an external effect has occurred.
Generation vs. Ranking and Selection
Generation creates candidates. Ranking compares them. Selection chooses the candidate preferred for downstream processing.
Separating these operations keeps behavioral policy explicit and observable.
Cognitive Inputs
Behavior generation can be influenced by reasoning, decisions, active goals, plans, working state, learning, and other cognitive signals.
It should therefore be understood as multi-signal synthesis rather than a direct Decision-to-Action conversion.
Actionability
A selected behavior may still require actionability, capabilities, permissions, dependencies, or governance before execution.
This separates what the agent prefers to do from what it is currently allowed and able to do.
Behavior Generation in Loomia
Loomia currently represents behavior options, ranking, selection, and selected behavior separately.
The behavior layer can incorporate reasoning, decision, learning, and planning state before downstream execution governance.
Behavior Generation in AI Agents
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.
glossaryDecision Making
Decision making is the cognitive process through which an AI agent evaluates alternatives and selects, defers, or rejects a course of action under goals, evidence, constraints, and uncertainty.
glossaryReasoning
The cognitive process through which an AI agent interprets context, evidence, memory, knowledge, goals, and internal state to derive structured inferences.
glossaryPlanning
Planning in an AI agent is the process of constructing and revising structured paths from the current state toward an active goal.
glossaryGoal Management
Goal management is the process through which an AI agent represents, prioritizes, monitors and governs desired future states across time.
glossaryAttention
Attention in an AI agent is the process that prioritizes which available observations, memories, goals and internal states receive cognitive processing.
glossaryWorking Memory
Working memory in an AI agent is the limited and dynamically updated information state currently available to active cognition.
glossaryCognitive Continuity
Cognitive continuity is the ability of an AI agent to preserve, retrieve, update and evolve the internal structures that influence its behavior across interactions and over time.
glossaryPersistent AI Agent
A persistent AI agent is an AI agent whose behavior can be influenced by durable internal state that survives individual interactions and can be retrieved, updated and evolved over time.
glossaryAction Execution
Action execution is the governed process through which an AI agent attempts to turn an operational action representation into an actual effect.