Contextual refinement of agentic ai model reasoning, goal and task provisioning, and response generation

A method for contextual refinement of agentic AI model reasoning and response generation, executed by a processor, comprising:

• retrieving a pairing of a plan hypothesis model and a contextual weighting sub-model based on a user-provided context;
• performing iterative refinement based on an intent derived from the user-provided context, the iterative refinement comprising:
• dereferencing the pairing from a corresponding source ontology, and referencing the pairing into an intermediate representation space;
• applying the pairing to a reference data model represented in the intermediate representation space, the applying producing a current construct;
• evaluating the current construct based on evaluation criteria derived from an archetype reference model and one or more of structural, semantic, or contextual consistency fidelity parameters;
• constructing a next pairing based on results of the evaluating and at least one of user inputs, relationships among aspects of any of prior or current pairings, prior or current instances of the reference data model, or prior or current constructs;
• parameterizing the next pairing;
• and
• repeating the iterative refinement until a produced construct satisfies the evaluation criteria;
• and
• storing the construct in an agentic AI model response generation data store.

A method for contextual refinement of agentic AI model reasoning and response generation, executed by a processor, comprising:

• retrieving a pairing of a plan hypothesis model and a contextual weighting sub-model based on a user-provided context;
• performing iterative refinement based on an intent derived from the user-provided context, the iterative refinement comprising:
• applying the pairing to a reference data model represented in an intermediate representation space, the applying comprising informatic convolution of informatic nodes of the pairing upon informatic nodes of the reference data model and producing a current construct;
• evaluating the current construct based on evaluation criteria derived from an archetype reference model and one or more of structural, semantic, or contextual consistency fidelity parameters;
• constructing a next pairing based on results of the evaluating and at least one of user inputs, relationships among aspects of any of prior or current pairings, prior or current instances of the reference data model, or prior or current constructs;
• parameterizing the next pairing;
• and
• repeating the iterative refinement until a produced construct satisfies the evaluation criteria;
• and
• storing the produced construct that satisfies the evaluation criteria in an agentic AI model response generation data store.