Decision Rights and Accountability in Multi-Agent AI: A Governance Model for Human Override, Escalation, and Auditability

Abstract

The growing adoption of multi-agent artificial intelligence (AI) systems is transforming organizational decision-making by
enabling autonomous agents to coordinate tasks, delegate responsibilities, access external tools, and execute actions with
limited human intervention. However, this increasing autonomy creates significant governance challenges concerning decision
authority, human oversight, accountability, escalation, and the traceability of agent-generated outcomes. This study develops a
governance model for defining decision rights and accountability in multi-agent AI environments, with particular emphasis on
human override, risk-based escalation, and end-to-end auditability. Drawing on concepts from AI governance, organizational
decision-rights theory, human–AI collaboration, responsible AI, and multi-agent systems, the study identifies key governance
requirements for managing distributed agency and delegated authority. The proposed model establishes differentiated levels
of agent decision authority, bounded delegation rules, risk-sensitive escalation mechanisms, and explicit conditions for human
approval, intervention, suspension, and override. It further introduces an accountability structure that distinguishes operational,
supervisory, technical, governance, and organizational responsibility across the multi-agent decision chain. To strengthen
transparency and post-decision review, the model incorporates decision provenance mechanisms that record agent identities,
interactions, delegations, tool usage, policy constraints, human interventions, and final outcomes. The study argues that effective
governance of multi-agent AI should not eliminate autonomy but should dynamically align autonomy with decision impact,
uncertainty, reversibility, and organizational risk. The proposed framework provides a foundation for organizations seeking
to deploy multi-agent AI systems while preserving meaningful human authority, accountability, regulatory compliance, and
auditable decision-making.