SAGA: Shapley-Governed Agentic Governance Architecture for Multi-Enterprise AI Coalitions

Autonomous AI agents working across enterprise boundaries today carry governance as an afterthought: access-control policies that are attached externally, can be circumvented, and give no neutral auditable record that all parties trust. This paper presents SAGA (Shapley-Governed Agentic Governance Architecture), a framework that embeds governance directly into agent identity by encoding each agent's Shapley-derived contribution score, permission tier, and coalition memberships into a cryptographically signed agent capability descriptor. SAGA rests on four contributions: governance-intrinsic agent identity extending the Google A2A and SAP ORD card schemas; a two-layer hierarchical Shapley governance system in which intra-enterprise LLM agent scores feed cross-enterprise org-agent characteristic functions; event-triggered self-healing governance where a violation atomically triggers Shapley recomputation, tier downgrade, coalition revocation, and card re-issuance in a single on-chain transaction; and a hybrid blockchain design combining Hyperledger Fabric for permissioned operational state with periodic Merkle-root anchoring to Ethereum for regulatory auditability, connected by a ZKP commitment scheme. A complete implementation is provided with 74 Python, 5 Go, and 4 Solidity tests all passing. Experiments report Monte Carlo approximation error converging at O(1/sqrt(k)), linear-time Shapley computation scaling, sub-millisecond ZKP commit-verify throughput, and quantitative violation-recovery dynamics across all governance tiers.