A Crisis of Confidence

Agentic artificial intelligence systems are advancing rapidly in capability, autonomy, tool use, planning, and multi-agent coordination. Yet confidence in the safe, reliable, secure, and mission-aligned operation of these systems is not advancing at the same pace. This creates an Assurance Gap: the difference between what autonomous AI systems can do and what can be demonstrated, validated, monitored, and assured about their behavior. This paper defines the Assurance Gap in Agentic AI and argues that it represents a central barrier to operational adoption. The gap is not merely a problem of model evaluation or benchmark performance. It arises from limited observability, weak runtime evidence, incomplete workflow conformance, insufficient validation environments, inadequate human oversight, and lack of assurance guarantees for distributed agentic systems. We introduce a confidence-oriented framework for agentic AI assurance and discuss foundational assurance guarantees including safety, liveness, recurrence, and convergence. The paper argues that trustworthy deployment of agentic AI requires measurable confidence grounded in operational evidence rather than capability claims alone.