Reasoning

Reasoning is the cognitive capability that allows an agent to determine what follows from the information currently available to it.

Instead of merely retrieving stored information, reasoning interprets relationships among context, evidence, memory, knowledge, goals, intentions, and internal state to produce new cognitive representations.

These representations can include conclusions, explanations, hypotheses, implications, predictions, comparisons, or assessments of uncertainty.

Retrieval selects information that may be relevant. Reasoning determines what can be inferred from that information.

An agent can retrieve the correct memory or knowledge and still reason incorrectly, making retrieval quality and reasoning quality separate architectural concerns.

Reasoning derives conclusions and implications. Decision making selects among alternatives using goals, policies, constraints, expected outcomes, or other criteria.

Separating inference from selection allows cognitive architectures to apply explicit governance after reasoning has occurred.

Reasoning determines what appears to follow from available evidence, while planning organizes future steps toward an objective.

Reasoning can inform plans and plans can create new reasoning problems, but the two represent different cognitive responsibilities.

Language models can provide semantic inference, interpretation, hypothesis generation, and integration of ambiguous evidence.

However, an LLM invocation is not by itself a complete reasoning architecture. Agent reasoning also depends on context selection, memory, knowledge, goals, evidence representation, uncertainty, downstream control, and persistence.

Reasoning should not be defined by whether a textual chain-of-thought is exposed.

A cognitive system can represent reasoning through structured conclusions, evidence, confidence, hypotheses, state, or other artifacts without exposing private model-internal reasoning.

Persistent agents reason across an evolving cognitive history rather than treating every interaction as an isolated prompt.

Their reasoning can incorporate previous experiences, consolidated knowledge, current goals, learning, and persistent state, while conclusions from one cycle can influence future cognition.

In Loomia's current runtime, Reasoning is an explicit cognitive stage after Goal Formation and before Decision and Planning.

The reasoning result remains separate from decision, planning, behavior, action, reflection, learning, experience, and persistent cognitive state.

This architecture treats reasoning as one cognitive capability within a larger autonomous loop rather than using reasoning as a synonym for the entire agent.

Loomia combines semantic inference with deterministic control mechanisms.

Semantic models can handle interpretation and open-ended inference, while deterministic mechanisms enforce policies, validation, scoring, invariants, lifecycle rules, and execution constraints.