Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Agentic AI systems introduce new accountability challenges because autonomous agents can act, adapt, and execute decisions without continuous human oversight. This research develops the Surrogate Accountability Framework (SAF), an architectural approach for embedding external oversight, traceability, and control directly within agentic workflows. To operationalize SAF, a working system, Chrysalis, was designed and implemented as a real time governance layer that monitors agent behavior, evaluates decision pressure, and enforces constraints through validation and intervention mechanisms. By shifting accountability from post hoc evaluation to continuous system level enforcement, this approach reduces the risk of compounding errors and enables interpretable, actionable control signals. The results demonstrate a practical pathway for deploying agentic AI systems with enforceable accountability in real world environments.
Included in
GC-173-232 A Surrogate Accountability Framework for Agentic AI Systems
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Agentic AI systems introduce new accountability challenges because autonomous agents can act, adapt, and execute decisions without continuous human oversight. This research develops the Surrogate Accountability Framework (SAF), an architectural approach for embedding external oversight, traceability, and control directly within agentic workflows. To operationalize SAF, a working system, Chrysalis, was designed and implemented as a real time governance layer that monitors agent behavior, evaluates decision pressure, and enforces constraints through validation and intervention mechanisms. By shifting accountability from post hoc evaluation to continuous system level enforcement, this approach reduces the risk of compounding errors and enables interpretable, actionable control signals. The results demonstrate a practical pathway for deploying agentic AI systems with enforceable accountability in real world environments.