US20260024037
2026-01-22
Physics
G06Q10/06316
The patent application describes a modular AI agent system designed for seamless integration with Software-as-a-Service (SaaS) platforms. This system automates data operations, synchronizes workflows across different platforms, and facilitates intent-based interactions. AI agents are enrolled as credentialed users, allowing them to read and write data while interacting with table structures that represent items and their characteristics linked to a common objective. The system guides these agents through structural relations and role profiles to generate editing instructions, detect data inconsistencies, and notify users or request additional information as needed.
The system employs hierarchical access schemes that support multiple AI agent instances with inherited privileges, managed through an AI center. These agents can function autonomously as team members, analyzing outputs and aiding in natural-language explanation sessions. The platform also supports inter-service updates, deviation detection, and the construction of tailored products and platform elements. These features enhance automation, decision support, and operational efficiency within complex SaaS environments.
Generative AI capabilities are integrated into SaaS platforms, enabling AI agents to interact with alphanumeric data in table structures. Each agent is associated with a role profile that guides its interactions according to a set of rules, which can be updated as needed. The system includes a credentials management process for defining user credentials, allowing AI agents to interact with data while maintaining control over access and actions. This setup facilitates intent-based interactions and supports dynamic project coordination.
The system features an AI center interface that displays multiple AI agents, allowing for the deployment of multiple instances as limited resources. This management ensures resource limits are not exceeded. Additionally, the system supports interactive analysis of AI outputs, storing outputs with metadata and offering user interfaces for initiating natural language interaction sessions. Users can query outputs, and the system provides explanatory responses, enhancing understanding and decision-making.
The system conducts contextual data analysis within structured environments, accessing data structures and analyzing column properties. An AI model performs contextual analysis, addressing challenges in data management and task automation. The system maintains essential controls for data privacy and security, transforming business interactions with SaaS platforms. These advancements lead to increased productivity, informed decision-making, and improved operational efficiency across various industries.