Creating Characteristically Auditable Agentic AI Systems

Auditability -the ability to reliably record, verify, and analyze agent behavior -is a key requirement for trustworthy multi-agent AI systems.This paper formalizes auditability with a set of eight axioms (Integrity, Coverage, Temporal Coherence, Verifiability, Accessibility, Resource Proportionality, Privacy Compatibility, and Governance Alignment) and extends the theory to incorporate liveness properties (ensuring eventual auditability under fault tolerance and agent failures) and adversarial resilience (game-theoretic resistance to strategic log manipulation).We situate this framework in the context of recent research on accountability, transparency, and traceability in distributed AI, and we identify design patterns that operationalize these auditability axioms in system architecture and agent behavior (including partial observability, asynchronous communication, and incentive constraints).All propositions are stated with formal rigor and proved inline.The resulting formalism provides a foundation for building multi-agent systems that are not only auditable by design but also robust against faults and adversaries, aligning with emerging governance requirements for AI.