AI agent system for dynamic replication and optimization of CRM workflows across heterogeneous platforms

Abstract

A client-agent-based system (100) for dynamically replicating and optimizing customer relationship management (CRM) workflows across heterogeneous platforms, the system comprising: a) an intelligent workflow extraction module configured to interface with multiple CRM platforms and extract existing workflow data and logic; b) a semantic workflow modeling engine configured to transform extracted workflows into a unified, platform-independent semantic model using standardized ontologies; c) a cross-platform workflow module configured to map the semantic model into platform-specific workflow constructs for different CRM target systems; (d) an optimization and adaptation module configured to analyze historical and real-time performance data and dynamically improve workflow configurations using artificial intelligence techniques; (e) an autonomous deployment and synchronization module configured to deploy and manage optimized workflows across multiple platforms while maintaining version control and execution consistency; (f) a contextual AI agent configured to manage intelligent agents embedded in CRM environments, enabling real-time monitoring, learning and adaptation of workflows; and g) an analytics and governance dashboard configured to provide administrators with operational insights, performance metrics, audit trails, and control over AI-driven decisions and workflow modifications.

Description

[0001] The present invention relates to the field of artificial intelligence and enterprise software systems. In particular, it relates to AI agent-based systems designed for dynamic replication, adaptation, and optimization of customer relationship management (CRM) workflows. The invention addresses the challenges of workflow interoperability and performance consistency across heterogeneous CRM platforms.

[0002] In today's enterprise environments, organizations often rely on multiple CRM platforms such as Salesforce, HubSpot, Zoho, or Microsoft Dynamics to manage customer interactions, sales, and service operations. These platforms differ significantly in terms of architecture, workflow logic, and API standards, creating significant challenges when synchronizing business processes across different systems. As a result, replicating or transferring optimized workflows from one platform to another is a highly manual, error-prone, and time-consuming process that requires specialized technical expertise.

[0003] Furthermore, CRM workflows must be continually optimized based on changing customer behavior, market conditions, and business goals. Traditional rule-based automation lacks adaptability and cannot self-adjust in real time, leading to inefficiencies and missed opportunities. Companies struggle to maintain consistent customer experience and operational efficiency if workflows cannot dynamically evolve or scale across platforms.

[0004] There is an urgent need for an intelligent, autonomous solution capable of understanding, replicating, and optimizing CRM workflows across heterogeneous platforms without human intervention. An AI agent-based system that can learn from existing workflows, adapt them contextually, and deploy optimized processes across multiple systems would significantly increase business agility, reduce operating costs, and ensure a seamless customer experience across diverse digital channels.

[0005] One goal of this disclosure is to enable seamless replication of CRM workflows across multiple heterogeneous platforms.

[0006] Another objective of this disclosure is to reduce manual effort and human errors in workflow migration and optimization.

[0007] Another objective of this disclosure is to improve workflow efficiency through continuous AI-driven performance optimization.

[0008] Another objective of this disclosure is to support real-time adaptation of workflows based on contextual insights and usage patterns.

[0009] Another objective of this disclosure is to ensure cross-platform compatibility through semantic modeling and intelligent translation.

[0010] Another goal of this disclosure is to enable autonomous deployment and synchronization with version control and rollback.

[0011] Another objective of this disclosure is to provide complete transparency and governance through an integrated analytics dashboard.

[0012] Another objective of this disclosure is to improve the customer experience by maintaining consistent and optimized CRM operations.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention provides an AI agent-based system for replicating and optimizing CRM workflows across different platforms. It eliminates manual reconfiguration and ensures consistent operational logic across different systems.

[0015] Another embodiment of the present invention is It uses intelligent extraction techniques to non-invasively capture workflow structures from existing CRM platforms.

[0016] Another embodiment of the present invention is that a semantic modeling engine transforms the extracted workflows into a platform-independent format, ensuring that the business meaning of each process is preserved in different CRM environments.

[0017] Another embodiment of the present invention is that the system includes a translation module that adapts workflows into the syntax and formats required by the target CRMs.

[0018] Another embodiment of the present invention is that AI-based optimization continuously refines workflows based on real-time and historical performance data.

[0019] Another embodiment of the present invention An autonomous deployment manager ensures secure, synchronized, and error-free rollout of workflows. It manages version control, rollback, and consistency across multiple CRM platforms.

[0020] Another embodiment of the present invention is context-aware AI agents that work in CRM systems to monitor and adapt workflows in real time. These agents learn from behavioral patterns and make intelligent decisions independently.

[0021] Another embodiment of the present invention is the centralized dashboard, which provides complete visibility, control, and auditability of AI decisions and workflow performance.

[0022] The present invention relates to an AI agent-based system for intelligently replicating, optimizing, and managing CRM workflows on heterogeneous platforms. It extracts existing workflows using AI techniques, models them into a unified semantic format, and translates them for compatibility with various CRM systems. The system continuously optimizes workflows based on real-time data and deploys them autonomously, while ensuring synchronization and version control. Embedded AI agents dynamically adapt workflows based on contextual insights and user behavior. A central dashboard provides transparency, governance, and control across the entire workflow lifecycle.

[0023] Intelligent workflow extraction module: This module uses AI-based pattern recognition, natural language processing (NLP), and process mining techniques to analyze existing CRM workflows from various platforms. It identifies and extracts workflow components such as triggers, conditions, actions, dependencies, and custom rules. The module is designed to operate non-intrusively using API integrations and data protocols, allowing it to seamlessly interpret workflow logic from heterogeneous systems without requiring in-depth platform-specific configurations.

[0024] Semantic workflow modeling module: Once workflows are extracted, this module transforms them into a unified, platform-independent semantic representation. Using ontologies and knowledge graphs, elements such as customer contact phases, lead evaluation logic, or escalation rules are standardized across platforms. This enables a cross-platform understanding of workflow intent and logic. The modeling engine ensures that workflows are replicated not only syntactically but also semantically, and that business meaning is preserved during replication and optimization.

[0025] Cross-platform workflow module: This module acts as a bridge between semantic models and specific CRM environments. It maps standardized workflow logic to native workflow structures, commands, and API formats of target platforms such as Salesforce, HubSpot, or Zoho. The translator includes a rule-based and machine learning-based mapping engine that adapts workflows to platform-specific capabilities and limitations, ensuring compatibility and operational efficiency without the need for manual reengineering.

[0026] Optimization and adaptation module: Using AI optimization algorithms, including reinforcement learning and heuristic strategies, this module improves workflows based on real-time data, KPIs, and historical performance metrics. It suggests changes such as refining trigger conditions, reordering tasks, or parallelizing to improve throughput, response time, or customer satisfaction. The engine continuously monitors performance and dynamically adapts workflows to ensure they remain efficient even in a changing operational context.

[0027] Autonomous provisioning and synchronization module: This module automates the deployment of optimized workflows across multiple CRM platforms while ensuring transaction integrity and synchronization. It manages version control, rollback mechanisms, and compatibility checks. Furthermore, it coordinates the simultaneous or staggered release of workflow updates across platforms to minimize disruption and align changes with business continuity requirements.

[0028] Context-dependent Kl agent: This module is the heart of the system and controls the operation of decentralized AI agents embedded in various parts of the CRM environment. These agents observe user behavior, platform activity, and external variables in real time and make decisions about workflow execution and transformation. The Orchestrator facilitates collaboration between agents, resolves conflicts, ensures compliance with business rules, and promotes emergent behavior for intelligent decision-making.

[0029] Analytics and Governance Dashboard: This module provides enterprise users with real-time visibility into workflow performance, system health, and cross-platform metrics. It includes customizable dashboards, anomaly detection alerts, and audit logs. Administrators can oversee AI decisions, approve critical changes, and ensure regulatory compliance. This module provides transparency and confidence in the autonomous operation of the AI ​​system.

[0030] The invention is explained again below with reference to the figure. It shows: Fig. : an AI agent system (100) for the dynamic replication and optimization of CRM workflows across heterogeneous platforms.

[0031] Fig.illustrates an AI agent system (100) for dynamic replication and optimization of CRM workflows across heterogeneous platforms. The AI ​​agent system for dynamic replication and optimization of CRM workflows operates through seamless orchestration of its intelligent modules to enable end-to-end automation across heterogeneous CRM platforms. First, the intelligent workflow extraction module connects to existing CRM systems via APIs and log data to non-invasively extract workflow structures, rules, and execution patterns. This extracted information is then processed by the Semantic Workflow Modeling Engine, which transforms the workflows into a unified, platform-independent representation using standardized ontologies.The cross-platform workflow module takes this semantic model and reconfigures it into native constructs compatible with different CRM target environments, taking into account differences in logic and functionality. At the same time, the optimization and adaptation engine analyzes real-time performance data and historical trends to refine workflows using AI algorithms, thus improving efficiency and customer retention. These optimized workflows are then automatically deployed by the Autonomous Deployment and Synchronization Module, which ensures versioning, rollback, and consistency across multiple platforms. The context-aware AI agent controls a network of intelligent agents embedded in CRM instances. This enables them to monitor ongoing processes, learn from usage patterns, and adapt workflows dynamically and on the fly.Finally, all activities and metrics are captured by the Analytics and Governance Dashboard, which allows administrators to monitor system health, review AI decisions, enforce compliance, and maintain control over AI-driven operations.

Claims

[1] A system (100) based on AI agents for dynamically replicating and optimising customer relationship management (CRM) workflows across heterogeneous platforms, the system comprising: a) an intelligent workflow extraction module configured to interface with multiple CRM platforms and extract existing workflow data and logic; b) a semantic workflow modeling engine configured to transform extracted workflows into a unified, platform-independent semantic model using standardized ontologies; c) a cross-platform workflow module configured to map the semantic model into platform-specific workflow constructs for different CRM target systems; (d) an optimization and adaptation module configured to analyze historical and real-time performance data and dynamically improve workflow configurations using artificial intelligence techniques; (e) an autonomous deployment and synchronization module configured to deploy and manage optimized workflows across multiple platforms while maintaining version control and execution consistency; (f) a contextual AI agent configured to manage intelligent agents embedded in CRM environments, enabling real-time monitoring, learning and adaptation of workflows; and g) an analytics and governance dashboard configured to provide administrators with operational insights, performance metrics, audit trails, and control over AI-driven decisions and workflow modifications. [2] The system (100) of claim 1, wherein the intelligent workflow extraction module uses process mining and natural language processing (NLP) techniques to identify workflow components such as triggers, actions, and conditional logic. [3] The system (100) of claim 1, wherein the semantic workflow modeling engine uses knowledge graphs to represent and relate workflow elements in a contextual manner. [4] The system (100) of claim 1, wherein the cross-platform workflow module includes a hybrid rule-based and machine learning mapping engine to ensure accurate conversion to platform-specific formats. [5] The system (100) of claim 1, wherein the optimization and adaptation module uses reinforcement learning to autonomously refine workflows based on defined key performance indicators (KPIs). [6] The system (100) of claim 1, wherein the autonomous deployment and synchronization module supports rollback operations and concurrent multi-platform deployment strategies. [7] The system (100) of claim 1, wherein the context-aware AI agent enables peer-to-peer communication between distributed agents to resolve workflow conflicts and adapt to system-wide changes. [8] The system (100) of claim 1, wherein the analytics and governance dashboard provides customizable visualizations, anomaly detection alerts, and role-based access controls. [9] The system (100) of claim 1, wherein the system is integrated with external data sources, including customer feedback systems, to refine workflow decisions in real time. [10] The system (100) of claim 1, wherein all modules operate in a secure, containerized, cloud-native environment that supports scalability, privacy compliance, and API-based extensibility.

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