Description:FIELD OF INVENTION
The present invention relates to the field of artificial intelligence and virtual agents. Specifically, the present invention relates to a system and method for creating, managing, and interconnecting virtual artificial intelligence agents with advanced memory systems, customizable behaviors, and workflow orchestration capabilities.

BACKGROUND OF THE INVENTION
A virtual artificial intelligence agent is an artificial intelligence -based software program that interacts with humans in a similar way to live agents. Virtual agents (also called virtual or voice assistants) can provide services and perform tasks based on different customer intents. Virtual agents can serve phone, chat, and text channels in a unified way. They augment human teams to provide a better experience for the customers and live agents. They take over repetitive customer interactions, freeing teams to solve cases only humans can solve.
Virtual artificial intelligence agents are increasingly used in diverse domains, including customer service, healthcare, education, and finance. Current systems, however, lack modularity, advanced memory integration, and the ability to seamlessly customize and orchestrate workflows among multiple agents.
PRIOR ART
In prior art US2012221504A1 - Computer implemented intelligent agent system, method and game system - A computer-implemented intelligent agent system is disclosed with long-term and short-term memory, where the data repository encodes behavioral attributes of the agent. This system processes input data, accesses the agent’s memory, and determines output based on the agent’s state, utilizing predefined memory ranges to define the agent’s behavioral state. While this system introduces memory integration, it lacks the ability to dynamically adjust or personalize agent behavior based on evolving data, limiting its capacity for task continuity and advanced personalization.
Another prior art - EP3828779A1 - Improved artificial intelligence system - presents a modular AI processing system comprising multiple sub-agent modules, each with an internal memory state and a communication algorithm. These sub-agent modules are designed to communicate with one another, enabling collaborative processing of input data. Additionally, the system includes a sub-agent spawning module to improve performance by replicating existing sub-agents based on specific metrics. While this modular structure supports communication between agents, it fails to provide a flexible system for real-time behavioral customization or efficient orchestration of workflows across agents, limiting its applicability in complex multi-agent environments.
While the prior art systems contribute to the field of intelligent agents, they do not address key limitations in the integration of memory systems, behavior customization, and workflow orchestration. The present invention uniquely integrates a dynamic memory architecture with context-specific embeddings and historical data graphs, offering advanced personalization and task continuity that the existing systems lack.

OBJECTS OF THE INVENTION:
The primary objective of the invention is to provide a system and method for creating, managing, and interconnecting virtual artificial intelligence agents with advanced memory systems, customizable behaviors, and workflow orchestration capabilities.
Another objective of the invention is to provide a system and method for creating, managing, and interconnecting virtual artificial intelligence agents that provides seamless integration of memory systems, behavior customization, workflow management, and enhanced inter-agent collaboration into a unified methodology, enabling unprecedented flexibility and scalability.

SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
This present invention introduces a comprehensive system and method for creating, managing, and interconnecting virtual AI agents equipped with advanced memory systems, customizable behaviors, and efficient workflow orchestration. The system is built around a Behavior Repository, which stores modular behavior modules that can be dynamically retrieved, validated, and loaded into an Agent Runtime environment for task execution.
The system incorporates a Knowledge Graph and Dynamic Context Embeddings to provide contextual insights and adaptability. These components support real-time decision-making processes and ensure that task execution aligns with evolving requirements. The Workflow Updates mechanism enables continuous refinement of task flows based on feedback and changes in the operational environment.
The system also leverages user inputs and a Large Language Model (LLM) to define task specifications and select appropriate agents for execution. Interaction protocols are established to facilitate seamless communication among agents, ensuring coordinated operations. Tasks are executed in a structured sequence or concurrently, depending on their dependencies, leading to efficient and scalable multi-agent workflows.

BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 shows the decision-making framework, including Ask Decision Making (10) for task evaluation, Knowledge Graph (20) for contextual insights, Dynamic Context Embeddings (30) for real-time adaptability, and Workflow Updates (40) for refining task execution based on feedback.
FIG. 2 shows the system's foundational elements, including the Behavior Repository (50), which stores predefined modules; Validate Compatibility (60), which ensures module suitability; Load Behavior Module (70), where validated modules are initialized; and Agent Runtime (80), the environment for executing these modules efficiently.
FIG. 3 shows the modular task structure with Task A (90) initiating operations, Task B (100) handling intermediate processing, Task C (110) performing critical actions, and Task D (120) delivering final outputs.
FIG. 4 shows depicts the interaction and execution process, starting with User Input (130), interpreted by LLM (140) into task specifications, followed by Agent Selection (160), Define Interaction Protocols (170), and task execution in Execute Workflow (180).

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
The present invention is directed to the field of artificial intelligence and virtual agents; specifically to a system and method for creating, managing, and interconnecting virtual artificial intelligence agents with advanced memory systems, customizable behaviors, and workflow orchestration capabilities.
FIG. 1 in accordance to the embodiment of the present invention depicts system and method for creating, managing, and interconnecting virtual artificial intelligence agents with advanced memory systems, customizable behaviors, and workflow orchestration capabilities provides seamless integration of memory systems, behavior customization, workflow management, and enhanced inter-agent collaboration into a unified methodology, enabling unprecedented flexibility and scalability.
According to the embodiment of the present invention, the memory system module is designed as a multi-layered structure. It comprises Dynamic Context Embeddings (DCE) [30] and Knowledge Graphs (KG) [20]. The Dynamic Context Embeddings (DCE) [30] are real-time representations of ongoing interactions using transformer-based models to capture context. The Knowledge Graphs (KG) [20] are storage of structured relationships and historical interactions, enabling long-term personalization and context-aware decision-making.
According to another embodiment of the present invention, in Dynamic Context Embeddings (DCE) (30), the input is Current operational context (C) , DCE buffer (E), and the output is Updated DCE buffer (E'). Firstly, embeddings for the Current operational context are generated using a transformer-based encoder. These embeddings are appended to the DCE buffer, maintaining a fixed-length sliding window. This DCE buffer is used to update the state of active workflows, and finally, the Updated DCE buffer is obtained.
In yet another embodiment, for Knowledge Graph Integration, the input is DCE buffer (E), Knowledge Graphs database (KG) (20), and the output is updated Knowledge Graphs [20]. For this integration, the DCE (30) buffer is analyzed for recurring entities, intents, and relationships. Then, the Knowledge Graphs (20) database is updated with newly identified patterns and enriched existing nodes. Finally, privacy and sensitivity filters are applied to ensure compliance.
In yet another embodiment as illustrated in Fig. 2, the present invention depicts a behavior customization workflow, wherein the behavior repository (50) serves as a centralized database for storing predefined behavior modules, wherein each module encapsulates distinct functional capabilities, enabling the system to perform diverse tasks efficiently, and is structured to allow rapid querying and retrieval based on compatibility with user inputs or task specifications, thereby ensuring scalability and adaptability for various applications; the compatibility validation (60) step ensures that behavior modules retrieved from the behavior Repository (50) align with the operational parameters of the system that involves matching the task's requirements with the metadata of the behavior module, verifying compatibility to guarantee seamless execution without runtime errors; the load behavior module (70) phase enables the system to fetch the validated module from the repository and loads it into the active runtime environment, thus, initializes the behavior module, allocating necessary computational resources and integrating it into the system's workflow; the agent runtime (80) is the execution environment for behavior modules that offers a stable and controlled platform for operation, featuring error-handling mechanisms and real-time performance monitoring, thereby ensuring optimal functioning of the behavior modules under varying conditions.
According to yet another embodiment of the present invention, the system employs a workflow for integration mechanism using a workflow engine, workflow creation module, and workflow execution module. The workflow engine acts as the central processing unit, enabling multiple agents to interact and execute tasks in series or parallel, where it organizes task workflows into a directed structure based on interdependencies, ensuring efficient task progression and optimal allocation of resources. The workflow engine enables the workflow creation module to design executable workflows by mapping task sequences to appropriate agents; wherein the module accepts the task sequence (T) and a list of participating agents (A_list) as inputs and generates an executable workflow (W) involving the following steps:
a. Representing the task sequence (T) as a directed acyclic graph (DAG), where nodes represent individual tasks and edges denote dependencies between them.
b. Mapping each task (node) to an agent from the A_list capable of executing the specific task, ensuring compatibility with the task's requirements.
c. Validating the compatibility of each task-agent pairing and ensuring data flow consistency between nodes in the DAG.
d. Generating an executable workflow (W) by combining the validated task-agent mappings with the dependency structure.

In yet another embodiment of the present invention, the workflow execution module oversees the real-time implementation of the generated workflow by utilizing the executable workflow (W) and input data (D), this module produces the task completion status (S) and results (R). The execution methodology involves the steps as follows:
a. Initialize the workflow (W) using the input data (D).
b. Execute tasks in sequence or parallel based on the dependency structure outlined in the DAG.
c. Pass intermediate results between dependent tasks to maintain data consistency and facilitate seamless progression through the workflow.
d. Aggregate the final results from all executed tasks and return the task completion status (S) along with the results (R).

As illustrated in Fig. 3, an exemplified workflow is provided, the tasks labeled Task A (90), Task B (100), Task C (110), and Task D (120), represent distinct operational units within the system such as:
● Task A (90) initiates the workflow, laying the groundwork for subsequent actions.
● Task B (100) processes intermediate data, building on outputs from Task A (90).
● Task C (110) executes critical operations that determine the primary outcomes.
● Task D (120) finalizes the process, compiling and delivering results.
In another preferred embodiment of the present invention as illustrated in Fig 4., the said system employs a stepwise method comprising the following steps:
1. User Input (130) - User Input (130) is the entry point for external commands and preferences, wherein the system processes these inputs to configure task specifications and align its operations with user expectations.
2. LLM (140) - The LLM (140) component integrates a large language model to interpret complex user inputs and translate them into actionable directives, ensuring that the system can handle a wide range of user requirements with precision.
3. Task Specification (150) - In the Task Specification (150) phase, the system formulates a detailed outline of the objectives and parameters for the task. This specification acts as a blueprint, guiding the subsequent stages of workflow execution.
4. Agent Selection (160) - The Agent Selection (160) stage identifies and activates the most suitable agents from the repository, ensuring that the selected agents possess the capabilities required for the specified tasks.
5. Define Interaction Protocols (170) - The Define Interaction Protocols (170) step establishes the rules and procedures for communication between agents and other system components, which ensure synchronized and efficient task execution.
6. Execute Workflow (180) - The Execute Workflow (180) phase represents the culmination of the process, wherein the system equipped with the selected agents and predefined protocols, performs the specified tasks to achieve the desired outcomes effectively and efficiently.

While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:CLAIMS:
We claim,
1. A system and method for creating, managing, and interconnecting virtual artificial intelligence agents, with advanced memory systems, customizable behaviors, and workflow orchestration capabilities; wherein a memory system module comprises a multi-layered structure including a first layer of Dynamic Context Embeddings (DCE) (30) and a second layer of Knowledge Graphs (KG) (20);
characterized in that:
the DCE (30) layer receives a current operational context (C) as input and generates real-time representations of ongoing interactions using transformer-based models to capture context, wherein the generated embeddings are appended to a DCE (30) buffer, maintaining a fixed-length sliding window, and the DCE (30) buffer is then used to update the state of active workflows;
the KG (20) layer stores structured relationships and historical interactions, enabling long-term personalization and context-aware decision-making; wherein the DCE (30) buffer integrates with the KG (20) layer by analyzing the DCE buffer for recurring entities, intents, and relationships; the identified patterns are used to update the KG (20) database; enriching existing nodes; privacy and sensitivity filters are applied to ensure compliance;
the modular behavior customization workflow includes-loading the behaviour modules (70) from the behaviour repository (50) and ensures its compatibility validation (60) and provide an agent runtime (80) execution environment for behavior modules that offers a stable and controlled platform for operation, featuring error-handling mechanisms and real-time performance monitoring;
the workflow for integration mechanism uses a workflow engine (200) with a workflow creation module, and workflow execution module; wherein the workflow engine acts as the central processing unit, enabling multiple agents to interact and execute tasks in series or parallel, where it organizes task workflows into a directed structure based on interdependencies, ensuring efficient task progression and optimal allocation of resources.

2. The system and method as claimed in claim 1, wherein the modular behavior customization workflow includes:
- the behavior repository (50) that serves as a centralized database for storing predefined behavior modules;
- the compatibility validation (60) step ensures that behavior modules retrieved from the behavior repository (50) align with the operational parameters that involves matching the task's requirements with the metadata of the behavior module, verifying compatibility to guarantee seamless execution without runtime errors;
- the load behavior module (70) enables the system to fetch the validated module from the repository and loads it into the active runtime environment, thus, initializes the behavior module;
- the agent runtime (80) execution environment for behavior modules that offers a stable and controlled platform for operation, featuring error-handling mechanisms and real-time performance monitoring, thereby ensuring optimal functioning of the behavior modules under varying conditions.

3. The system and method as claimed in claim 1, wherein the behavior modules of the modular behavior customization workflow encapsulates distinct functional capabilities, enabling the system to perform diverse tasks efficiently, and is structured to allow rapid querying and retrieval based on compatibility with user inputs or task specifications, thereby ensuring scalability and adaptability for various applications, and further comprises a mechanism for versioning and rollback of behavior module updates.

4. The system and method as claimed in claim 1, wherein the workflow engine (200) enables the workflow creation module to design executable workflows by mapping task sequences to appropriate agents; wherein the module accepts the task sequence (T) and a list of participating agents (A_list) as inputs and generates an executable workflow (W) involving the steps of;
a. representing the task sequence (T) as a directed acyclic graph (DAG), where nodes represent individual tasks and edges denote dependencies between them;
b. mapping each task (node) to an agent from the A_list capable of executing the specific task, ensuring compatibility with the task's requirements;
c. validating the compatibility of each task-agent pairing and ensuring data flow consistency between nodes in the DAG;
d. generating an executable workflow (W) by combining the validated task-agent mappings with the dependency structure.
5. The system and method as claimed in claim 1, wherein the workflow execution module of the workflow engine (200) executes a methodology involving the steps of;
a. initialize the workflow (W) using the input data (D);
b. execute tasks in sequence or parallel based on the dependency structure outlined in the DAG;
c. pass intermediate results between dependent tasks to maintain data consistency and facilitate seamless progression through the workflow;
d. aggregate the final results from all executed tasks and return the task completion status (S) along with the results (R).

6. The system and method as claimed in claim 1, wherein the task specifics are defined using natural language prompts that are interpreted by the LLM (140) such that the LLM (140) used to generate candidate agents for a given task and rank them based on their suitability.

7. The method as claimed in claim 1 comprises the steps of;
- processing user inputs (130) including external commands and preferences to configure task specifications and align its operations with user expectations;
- integrating the LLM (140) component to interpret complex user inputs and translate them into actionable directives, ensuring that the system can handle a wide range of user requirements with precision;
- formulating a detailed outline of the objectives and parameters using the task specification (150) phase for the specific task, which acts as a blueprint, guiding the subsequent stages of workflow execution;
- identifying and activating the most suitable agents from the repository, using the agent selection (160) step ensuring that the selected agents possess the capabilities required for the specified tasks;
- establishing the rules and procedures for communication between agents and other system components using the define interaction protocols (170) step which ensures synchronized and efficient task execution;
- executing workflow (180) thereby representing the culmination of the process, wherein the system equipped with the selected agents and predefined protocols, performs the specified tasks to achieve the desired outcomes effectively and efficiently.
Dated this 6th day of January, 2025.