Executive Summary
This paper examines the challenges of testing and evaluating agentic AI systems in military Command and Control (C2). It identifies eight assumptions underlying established T&E practices that agentic properties weaken, affecting the inference from tested to fielded behavior. The authors propose ten assurance claims and governance mechanisms to address these gaps, acknowledging that some evidence can only be generated in service.
Why It Matters
This document is crucial for defense analysts as it dissects the profound challenges of assuring the reliability and safety of agentic AI systems in critical military C2 functions, directly impacting future operational effectiveness and risk management. It highlights how current T&E methods are insufficient for these dynamic systems, necessitating new approaches for deployment decisions.
Key Takeaways
- Agentic AI systems weaken fundamental assumptions of traditional Testing and Evaluation (T&E) methods, making it difficult to infer fielded behavior from test results.
- Assurance for agentic AI in C2 requires narrower, trajectory-grounded claims and a shift of some evidentiary burden into continuous in-service monitoring and governance.
- The inherent dynamism, adaptability, and multi-agent coordination of these systems introduce complexities like emergent behaviors and evolving states that challenge current certification paradigms.
Strategic Relevance
The strategic relevance lies in ensuring the trustworthiness and predictability of AI systems integrated into military command and control. Failure to adequately test and evaluate these systems could lead to catastrophic operational failures, loss of human oversight, and unintended escalations, directly impacting national security and strategic stability.