Systems and methods for generating user-specific outputs using modular AI agents and hybrid data representation engines

1 . A method, comprising:

receiving, by at least one processor, a plurality of user data inputs and activity configuration data from a plurality of data sources, wherein the activity configuration data specifies at least one user-specific activity for at least one user;

processing, by the at least one processor, the plurality of user data inputs and the activity configuration data to generate input data for the at least one user-specific activity;

transmitting, by the at least one processor, the input data and the activity configuration data to a control plane module, wherein

the control plane module is configured to determine processing parameters for a plurality of modular AI agents, and

the processing parameters are based on the activity configuration data;

using, by the at least one processor, the plurality of modular AI agents to:

activate a first agent from the plurality of modular AI agents to extract at least one user interaction feature from the input data;

activate a second agent from the plurality of modular AI agents to convert numerical values within the input data into a text format; and

activate a third agent from the plurality of modular AI agents to generate a data representation with activity-specific information, wherein the generation of the data representation is based on the at least one user interaction feature and the numerical values converted to the text format;

generating, by the at least one processor, using the third agent and a hybrid data representation engine, at least one user-specific activity-specific data output from the data representation based on the activity configuration data;

evaluating, by the at least one processor, using the third agent and a large language model, the at least one user-specific activity-specific data output based on at least one evaluative criterion;

storing, by the at least one processor, the at least one user-specific activity-specific data output in a persistent storage layer; and

providing, by the at least one processor, the at least one user-specific activity-specific data output via an application programming interface (API) for subsequent use by downstream systems, wherein the API controls an interaction with a plurality of external entities.

2 . The method of claim 1 ,

wherein the first agent is a feature selection agent;

wherein the second agent is a numerical transformation agent;

wherein the third agent is a summarization agent; and

wherein the hybrid data representation engine is a hybrid summarization engine.

3 . The method of claim 2 , wherein the feature selection agent extracts the at least one user interaction feature from the input data using at least one of machine learning models or statistical algorithms.

4 . The method of claim 2 , wherein the numerical transformation agent converts the numerical values within the input data into the text format using at least one of predefined templates or algorithmic transformations.

5 . The method of claim 1 , wherein the at least one evaluative criterion comprises at least one of: a contextual relevance criterion, a factual accuracy criterion, or an alignment criterion with the at least one user-specific activity.

6 . The method of claim 1 , wherein the plurality of user data inputs comprises at least one of interaction logs, transaction records, sensor data, or unstructured text data.

7 . The method of claim 1 , wherein the activity configuration data includes operational constraints, data processing rules, or output formatting requirements.

8 . The method of claim 1 , wherein the hybrid data representation engine combines extractive techniques, comprising keyword identification and ranking, with abstractive techniques, comprising text generation and paraphrasing.

9 . The method of claim 1 , wherein the persistent storage layer indexes the at least one user-specific activity-specific data output for data integrity.

10 . The method of claim 1 , wherein the API further controls at least one of batch processing, real-time requests, or caching mechanisms, to optimize response times for frequently accessed outputs.

11 . A system, comprising:

at least one processor;

a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to:

receive a plurality of user data inputs and activity configuration data from a plurality of data sources, wherein the activity configuration data specifies at least one user-specific activity for at least one user;

process the plurality of user data inputs and the activity configuration data to generate input data for the at least one user-specific activity;

transmit the input data and the activity configuration data to a control plane module, wherein

the control plane module is configured to determine processing parameters for a plurality of modular AI agents, and

the processing parameters are based on the activity configuration data;

use the plurality of modular AI agents to:

activate a first agent from the plurality of modular AI agents to extract at least one user interaction feature from the input data,

activate a second agent from the plurality of modular AI agents to convert numerical values within the input data into a text format, and

activate a third agent from the plurality of modular AI agents to generate a data representation with activity-specific information, wherein the generation of the data representation is based on the at least one user interaction feature and the numerical values converted to the text format;

generate, using the third agent and a hybrid data representation engine, at least one user-specific activity-specific data output from the data representation based on the activity configuration data;

evaluate, using the third agent and a large language model, the at least one user-specific activity-specific data output based on at least one evaluative criterion;

store the at least one user-specific activity-specific data output in a persistent storage layer; and

provide the at least one user-specific activity-specific data output via an application programming interface (API) for subsequent use by downstream systems, wherein the API controls an interaction with a plurality of external entities.

12 . The system of claim 11 ,

wherein the first agent is a feature selection agent;

wherein the second agent is a numerical transformation agent;

wherein the third agent is a summarization agent; and

wherein the hybrid data representation engine is a hybrid summarization engine.

13 . The system of claim 12 , wherein the feature selection agent extracts the at least one user interaction feature from the input data using at least one of machine learning models or statistical algorithms.

14 . The system of claim 12 , wherein the numerical transformation agent converts the numerical values within the input data into the text format using at least one of predefined templates or algorithmic transformations.

15 . The system of claim 11 , wherein the at least one evaluative criterion comprises at least one of: a contextual relevance criterion, a factual accuracy criterion, or an alignment criterion with the at least one user-specific activity.

16 . The system of claim 11 , wherein the plurality of user data inputs comprises interaction logs, transaction records, sensor data, or unstructured text data.

17 . The system of claim 11 , wherein the activity configuration data includes operational constraints, data processing rules, or output formatting requirements.

18 . The system of claim 11 , wherein the hybrid data representation engine combines extractive techniques, comprising keyword identification and ranking, with abstractive techniques, comprising text generation and paraphrasing.

19 . The system of claim 11 , wherein the persistent storage layer indexes the at least one user-specific activity-specific data output for data integrity.

20 . The system of claim 11 , wherein the API further controls at least one of batch processing, real-time requests, or caching mechanisms, to optimize response times for frequently accessed outputs.