Evaluating Bounded Autonomy in Regulated Agentic AI: A Diagnostic Harness with Constitutional Rewards, Escalation Labels, and Runtime Governance
Factual evidence
What the source reports
Researchers introduced RegLLM, a diagnostic harness that evaluates bounded autonomy in regulated agentic workflows via runtime governance.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 30 September 2026
- Collected by OneBench
- 1 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2609.37501v1 Announce Type: new Abstract: We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation validity,…
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OneBench interpretation
Institutional assessment
So what
Diagnostic frameworks for agentic runtime governance offer technical patterns for validating multi-step autonomous workflows in regulated operations.
Do what
Review the evaluation signals with the team responsible for model risk management when building validation standards for agentic systems.