Agentic AI Reliability and Evaluation

The field of agentic AI is moving towards a more comprehensive understanding of reliability and evaluation, with a focus on developing frameworks and metrics that go beyond accuracy. Researchers are exploring the challenges of dynamic environments, inconsistent task execution, and unpredictable emergent behaviors, and are working to develop more robust and efficient systems. A key area of innovation is the development of holistic evaluation frameworks that consider multiple dimensions such as cost, latency, efficacy, assurance, and reliability. Notable papers in this area include:

  • Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems, which proposes a CLEAR framework for evaluating agentic AI systems in enterprise settings.
  • Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions, which introduces a new testbed for evaluating an agent's ability to model its environment and make strategic decisions.

Sources

Looking Forward: Challenges and Opportunities in Agentic AI Reliability

High-level reasoning while low-level actuation in Cyber-Physical Systems: How efficient is it?

Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems

Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions

Built with on top of