Autonomous AI Agent Validation with Dynamic Rule Sets
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Solution Overview
Problem
Existing software development systems lack intuitive and reliable methods for selecting appropriate generative machine learning models and validating their outputs, leading to inefficiencies, security breaches, and compliance challenges with evolving regulatory standards, particularly in high-risk sectors like finance and healthcare.
Innovation Solution
A data generation platform that dynamically evaluates machine learning prompts and validates outputs using AI models to ensure compliance with regulatory standards, mitigate security risks, and reduce inefficiencies by continuously monitoring and modifying agent actions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous AI agents operate with high autonomy to respond quickly to environmental changes, then productivity and responsiveness are improved, but reliability and correctness of actions deteriorate
Solution Approach 1:
The patent introduces a validation system comprising a validator component and a rule set that acts as an intermediary between the autonomous AI agent and its actions. The validator intercepts, validates, and potentially modifies agent actions before execution by checking them against predefined rules, policies, and regulatory standards. This mediator ensures that while the agent maintains autonomy and responsiveness, its actions are constrained to be reliable and compliant, thus resolving the contradiction between high autonomy/productivity and reliability.
2Reliability
If generative AI models are used to validate AI agent actions, then reliability and compliance are improved, but device complexity and computational resources worsen
Solution Approach 1:
The patent employs dynamic rule sets that can be updated and modified without requiring changes to the underlying validation system architecture. The rule set is configured to reflect evolving regulatory standards, organizational policies, and compliance requirements, allowing the system to adapt dynamically to changing requirements. This dynamic approach enables the system to maintain high reliability and compliance while avoiding the complexity of redesigning the entire validation infrastructure, as the same core validation mechanism handles different rule sets through configuration rather than structural changes.
Data Source
AI summary
The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.