AI Agent Evaluation Framework for LLM-Generated Adversarial Testing
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Solution Overview
Problem
The complexity and unpredictability of AI agents, driven by non-deterministic outputs and dynamic learning, complicate thorough testing, especially in real-world scenarios, making it challenging to ensure reliable, secure, and ethical performance.
Innovation Solution
An AI agent evaluation framework that leverages Large Language Models (LLMs) to generate a large number of test questions and simulate various attack scenarios, enhancing the testing process by improving the ability to test a high number of queries and evaluate AI agents against potential vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used for AI agents, then testing process is simple, but testing coverage is insufficient due to non-deterministic outputs and dynamic learning
Solution Approach 1:
The patent creates synthetic test requests by copying and transforming seed requests through an LLM-based generation process. The system generates variations of test requests (positive, negative, edge cases) based on templates and transforms them to match the target AI agent's interface, enabling comprehensive testing without manually creating every test case.
Solution Approach 2:
The testing system uses an LLM to automatically generate test requests, execute them, evaluate responses, and identify gaps in coverage. The system self-manages the entire testing process including request generation, transformation, execution, and analysis, reducing the need for manual intervention and complex external testing frameworks.
2Reliability
If comprehensive testing of AI agents is performed, then reliability and security are improved, but time consumption increases
Solution Approach 1:
The system pre-generates a comprehensive set of test requests including positive cases, negative cases, edge cases, and attack scenarios before actual testing. By preparing test data in advance using LLM transformations, the system avoids time-consuming on-the-fly request generation during the testing phase.
Solution Approach 2:
The testing framework continuously monitors and evaluates agent responses in real-time, automatically feeding results back into the system to generate refined test requests. This continuous feedback loop ensures thorough testing while maintaining efficient throughput by parallelizing test execution and immediately utilizing results.
3Adaptability or versatility
If AI agents are tested against diverse scenarios, then adaptability is improved, but difficulty in detecting and measuring performance increases
Solution Approach 1:
The patent introduces an LLM-based evaluation system as an intermediary between the AI agent and the testing framework. This intermediary automatically analyzes agent responses, compares them against expected outcomes, and generates detailed performance reports. The intermediary handles the complexity of measuring performance across diverse scenarios by transforming complex evaluation criteria into automated comparisons.
Data Source
AI summary
Systems and methods for an Artificial Intelligence (AI) agent evaluation framework include operating, in a test environment, an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; providing the AI agent with one or more requests; receiving a response to each of the one or more requests; and evaluating performance of the AI agent based on responses to each of the one or more requests. The one or more requests can be LLM-generated variations of a seed request either for testing the AI agent's ability to respond to queries or to test the ability of the AI agent to ignore malicious requests.