AI Agent Workflow Builder for Policy-Governed Model Integration
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Solution Overview
Problem
Current AI technologies lack effective systems for managing and integrating various datasets and models, requiring frequent code rewrites for new models, and there is no easy way to manage access and deployment of AI agents, especially in enterprise environments, leading to inefficiencies and security concerns.
Innovation Solution
A configurable AI platform that manages AI agents through a user interface, allowing users to create, test, and deploy AI agents using management policies, integrating AI models, datasets, and code objects, with features like geofencing, dependency tracking, and compliance management to ensure secure and efficient workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If custom workflows are coded using programming libraries to utilize AI models, then automation capability is improved, but code maintenance complexity increases due to frequent model updates requiring code rewrites
Solution Approach 1:
The patent introduces an intermediary layer (workflow builder and abstraction layer) between users and AI models. This intermediary allows users to create workflows using visual interfaces and standardized components rather than directly coding against specific model APIs. When models update, the intermediary absorbs the changes through updated adapters or connectors, shielding the core workflow logic from breaking changes.
Solution Approach 2:
The workflow builder creates universal workflow definitions that can execute across multiple AI model versions and types. The system designs workflows with model-agnostic interfaces and uses configuration files to specify model parameters, allowing the same workflow to adapt to different model versions without requiring code rewrites.
2Adaptability or versatility
If new and more powerful AI models are rapidly developed, then model capability is improved, but workflow compatibility deteriorates requiring repeated code rewrites
Solution Approach 1:
The system implements dynamic model configuration where workflows can adapt to different model versions through configuration files and parameter specifications. The workflow builder allows dynamic selection and configuration of model parameters, enabling workflows to remain compatible as models evolve without requiring structural changes to the workflow definitions.
Solution Approach 2:
The patent uses parameter-based model specification where workflows define model behavior through configurable parameters rather than hard-coded model-specific logic. This allows model parameters to be changed or updated without affecting the core workflow structure, maintaining compatibility across model versions through parameter adjustments.
3Reliability
If access control is implemented for datasets and models in enterprise environments, then security is improved, but system complexity increases due to managing multiple user permissions and policies
Solution Approach 1:
The patent combines access control policies with the workflow definition process. The system merges security requirements into the workflow configuration, where permissions and access policies are specified as part of the workflow metadata. This integration allows centralized policy management without adding separate complex access control systems.
Solution Approach 2:
The workflow builder provides self-service access control configuration where users can define their own permission requirements and access policies when creating workflows. The system automatically manages permission inheritance and policy application based on user roles and workflow requirements, reducing the administrative burden of manual access management.
4Ease of operation
If visual workflow builder is provided for creating AI agents, then ease of use is improved, but learning curve for advanced features may increase
Solution Approach 1:
The workflow builder segments the workflow creation process into distinct, manageable components and steps. Users can build workflows incrementally using modular components, testing and validating each segment independently. This segmentation allows users to master basic features first and gradually progress to advanced capabilities without being overwhelmed by the entire system at once.
Data Source
AI summary
Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.