Agentic Application Evaluation for Black-Box Output Verification
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Solution Overview
Problem
There is no reliable or efficient way to evaluate or improve the performance of agentic applications, which operate as black boxes, generating outputs without insight into how they were generated, and there is no method to verify the accuracy or helpfulness of their outputs.
Innovation Solution
An evaluation system that assesses agentic applications across multiple metrics, including correctness, relevance, and helpfulness, using a customizable framework that can test on diverse test cases and adapt to different contexts, with feedback used to retrain models and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agentic applications operate as black boxes to execute workflows dynamically, then productivity and adaptability are improved, but reliability and measurement precision deteriorate because there is no way to verify output accuracy or understand how outputs were generated
Solution Approach 1:
The patent introduces an evaluation system as an intermediary between the agentic application and the user. This evaluation system includes evaluator applications that act as mediators to assess the outputs generated by the agentic application, providing verification of accuracy and quality without interfering with the dynamic workflow execution capability of the original application.
Solution Approach 2:
The patent implements a feedback mechanism where the evaluation system generates evaluation results that are fed back to improve the agentic application. The feedback includes quality assessments, accuracy measurements, and performance metrics that enable continuous improvement of the application's output reliability while maintaining its dynamic workflow execution capabilities.
2Adaptability or versatility
If agentic applications dynamically construct workflows to provide wide range of assistance, then adaptability is improved, but device complexity increases making evaluation and improvement difficult
Solution Approach 1:
The patent segments the evaluation system into multiple independent evaluator applications, each responsible for evaluating specific aspects of the agentic application's output. This segmentation allows the system to handle diverse task types through specialized evaluators while maintaining overall system manageability and reducing the complexity burden of evaluating dynamic workflows.
Solution Approach 2:
The evaluation system is designed with universal components that can handle multiple types of tasks and output formats. The evaluator applications are configured to assess various kinds of workflows, tools, and data sources using common evaluation frameworks, reducing the need for separate evaluation mechanisms for each specific task type.
3Reliability
If comprehensive testing is performed to validate agentic applications before release, then reliability is improved, but loss of time increases during the testing phase
Solution Approach 1:
The patent implements preliminary action by performing automated evaluation and testing of the agentic application before it is deployed to production. The evaluation system conducts comprehensive assessments in advance, identifying potential issues and areas for improvement before the application goes live, thereby ensuring reliability while managing testing time through proactive validation.
Data Source
AI summary
The subject technology includes an evaluation system for agentic applications. The evaluation system may use one or more evaluation applications to grade the performance of an agentic application based on one or more performance metrics. Scores determined for individual metrics may be combined using a set of weights to tailor the importance of each metric in the overall performance evaluation to a particular industry or application. An optimization engine may improve the performance of target agentic applications that are deficient in one or more metrics by training a portion of the agent application on a training dataset that includes example responses that score well for the one or more metrics where the target applications are deficient.