Benchmarking and Evaluation (Agent Ratings)

Part of our Loyal Agents collaboration with the Consumer Reports Innovation Lab

Building, Testing & Evaluating “Loyalty” in Sandboxes Using LLM-as-Judge Approach

Using an LLM as a judge means using one large language model to evaluate the quality of another LLM’s output based on specific criteria. This is a scalable and fast way to automate the quality control of AI systems, such as chatbots or content generators, by providing automated scoring, ranking, or feedback on tasks like accuracy, relevance, and tone. For example, a “judge” LLM can be given a user query, the original model’s response, and a set of rules to provide a score or a qualitative assessment.

This part of the Loyal Agents project aims to:

  • Implement an agent judge with tools that use the Loyal Agents Handbook + specific scenarios to detect potential risk in agent interactions
  • Design simulated environments to test the judge
  • Design real-world environments to detect risk

Open Source Repos for LLM-as-a-judge implementations:

Developing an Open Neutral Rating Service for Qualifying Agent Capabilities and Performance

With thousands of agents already available, users need to know the performance and level of legal duty (care, loyalty, etc) achieved through those agents. All agentic AI companies are beginning to recognize this as key for a healthy ecosystem.

This part of the Loyal Agents project aims to:

  • Develop benchmarks and convene companies to establish industry standards
  • Cryptographically secure ratings for legal status and performance capability

Resources

Visit our Loyal Agents page