Population
Enterprise-style agent workflows that access multiple tools, data sources, and permission levels.
All projects
seeking partner
How much privilege should an LLM agent receive before a human or policy gate intervenes?
Test least-privilege, just-in-time authorization and consequence-aware approval patterns for LLM agents that use enterprise tools and data.
HYPOTHESIS
Task-scoped privileges with consequence-aware approval will reduce excessive or policy-inconsistent agent actions by at least 30% while increasing median task completion time by no more than 10%.
PRIMARY METRIC
Successfully completed workflows without excessive privilege use or policy violation.
MEANINGFUL THRESHOLD
At least 30% fewer excessive or policy-inconsistent actions with no more than 10% increase in median completion time.
BUSINESS TARGET
A measurable human-control policy for scaling enterprise LLM agents without granting unnecessary standing privilege.
DECISION RULES
Security threshold met with acceptable workflow cost
Pilot in one bounded enterprise workflow.
Risk falls but approval burden is too high
Raise or personalize the consequence threshold.
No material risk reduction
Do not add the approval pattern as designed.
Business outcomes are research targets, not guarantees. A null or negative result may still create substantial value by preventing investment in an ineffective product, feature, or campaign.
Intervention
Task-scoped permissions, explicit agent identity, and approval only when an action crosses a predefined consequence threshold.
Comparator
Broad standing permissions with a generic confirmation step.
Secondary metrics
Task completion time · Approval burden · Corrective intervention · Auditability of agent actions