AGENTIC INTERMEDIARY FOR MANAGING AI PROVIDERS

1 - 20 . (canceled)

21 . A method for orchestrating requests across multiple artificial intelligence (AI) models, comprising:

receiving a complex request exceeding a complexity threshold;

decomposing the complex request into a plurality of subtasks;

generating a task dependency graph defining an execution order for the plurality of subtasks;

selecting, for each subtask, a respective AI model from a plurality of available AI models based on respective task-specific requirements and capabilities of the respective AI model;

routing the each subtask to its respective selected AI model according to the task dependency graph, wherein at least two of the plurality of subtasks are processed in parallel;

aggregating responses from the respective selected AI models for the plurality of subtasks to form a consolidated response; and

transmitting the consolidated response to a requester.

22 . The method of claim 21 , wherein selecting the respective AI model for each subtask comprises:

retrieving client-defined parameters from a configuration database; and

filtering a set of available AI models based on both the task-specific requirements and the retrieved client-defined parameters.

23 . The method of claim 21 , wherein selecting the respective AI model comprises:

evaluating real-time performance metrics, cost parameters, and availability of each AI model.

24 . The method of claim 21 , further comprising:

retrieving context data from at least one of a short-term memory, a long-term memory, or a vector database; and

augmenting one or more subtasks with the retrieved context data before routing.

25 . The method of claim 21 , further comprising:

anonymizing sensitive data in the complex request or any of the subtasks prior to transmitting them to an external AI model.

26 . The method of claim 21 , further comprising:

caching one or more subtask responses for use in similar future requests to reduce latency and cost.

27 . The method of claim 21 , further comprising:

dynamically adjusting the task dependency graph during execution based on intermediate results or changes in system conditions.

28 . The method of claim 21 , further comprising:

in response to determining that a selected AI model is unavailable or fails to meet performance criteria, selecting an alternate AI model.

29 . A system comprising:

a memory subsystem; and

processing circuitry configured to execute instructions stored in the memory subsystem to orchestrate requests across multiple artificial intelligence (AI) models, the instructions comprising to:

receive a complex request exceeding a complexity threshold;

decompose the complex request into a plurality of subtasks;

generate a task dependency graph defining an execution order for the plurality of subtasks;

select, for each subtask, a respective AI model from a plurality of available AI models based on respective task-specific requirements and capabilities of the respective AI model;

route the each subtask to its respective selected AI model according to the task dependency graph, wherein at least two of the plurality of subtasks are processed in parallel;

aggregate responses from the respective selected AI models for the plurality of subtasks to form a consolidated response; and

transmit the consolidated response to a requester.

30 . The system of claim 29 , wherein the processing circuitry further configured to execute instructions stored in the memory subsystem to:

analyze context requirements for at least one subtask of the plurality of subtasks;

retrieve context data from at least one data source, the at least one data source including one or more of a short-term memory store, a long-term memory store, or an internal knowledge base;

format the context data for compatibility with the respective selected AI model for the at least one subtask; and

route the formatted context data with the at least one subtask to the respective selected AI model.

31 . The system of claim 29 , wherein, to select the respective AI model for each subtask, the processing circuitry further configured to execute instructions stored in the memory subsystem to:

retrieve client-defined parameters from a configuration database;

identify a set of eligible AI models from an AI models register based on the client-defined parameters and the respective task-specific requirements;

evaluate the set of eligible AI models based on real-time availability and performance metrics; and

select an optimal AI model from the set of eligible AI models based on the evaluation.

32 . The system of claim 29 , wherein the processing circuitry further configured to execute instructions stored in the memory subsystem to:

dynamically adjust the execution order of the plurality of subtasks during processing based on intermediate results from at least one of the respective selected AI models, wherein the adjusting includes re-sequencing at least one subtask in the task dependency graph.

33 . The system of claim 29 , wherein, to decompose the complex request into the plurality of subtasks, the processing circuitry further configured to execute instructions stored in the memory subsystem to:

use an internal AI model to analyze a semantic structure of the complex request to identify action verbs, target objects, and contextual constraints; and

map the semantic structure to predefined task templates stored in a configuration database to generate the plurality of subtasks.

34 . The system of claim 29 , wherein the processing circuitry further configured to execute instructions stored in the memory subsystem to:

schedule the routing of at least one subtask using a scheduling engine to queue the at least one subtask for asynchronous execution based on system load and task priority.

35 . The system of claim 29 , wherein the complex request includes a request to analyze data and generate a report, and wherein the plurality of subtasks includes at least:

a first subtask for retrieving data from a database;

a second subtask for analyzing the data using a first AI model specialized in data analysis; and

a third subtask for generating the report using a second AI model specialized in natural language generation

36 . One or more non-transitory computer readable media storing instructions operable to cause one or more processors to perform operations for orchestrating requests across multiple artificial intelligence (AI) models, the operations comprising:

receiving a complex request exceeding a complexity threshold;

decomposing the complex request into a plurality of subtasks;

generating a task dependency graph defining an execution order for the plurality of subtasks;

selecting, for each subtask, a respective AI model from a plurality of available AI models based on respective task-specific requirements and capabilities of the respective AI model;

routing the each subtask to its respective selected AI model according to the task dependency graph, wherein at least two of the plurality of subtasks are processed in parallel;

aggregating responses from the respective selected AI models for the plurality of subtasks to form a consolidated response; and

transmitting the consolidated response to a requester.

37 . The one or more non-transitory computer readable media of claim 36 , wherein decomposing the complex request into a plurality of subtasks comprises:

analyzing the request using an orchestrating agent configured to identify logical tasks and dependencies.

38 . The one or more non-transitory computer readable media of claim 36 , wherein generating the task dependency graph comprises:

constructing a directed acyclic graph representing task execution order and interdependencies.

39 . The one or more non-transitory computer readable media of claim 36 , wherein selecting the respective AI model for each subtask comprises:

retrieving client-defined preferences from a configuration database and filtering AI models based on the preferences

40 . The one or more non-transitory computer readable media of claim 36 , wherein selecting the respective AI model includes evaluating real-time performance metrics, cost parameters, and availability of each AI model.