Building Trust in Agentic AI: TRACE Framework for Policy-Driven Multi-Agent System Design
The rapid adoption of multi-agent AI systems— ranging from prescriptive, workflow-driven deployments to fully agentic, autonomous ecosystems—raises urgent challenges for trust, accountability, and regulatory compliance. This paper introduces the TRACE Framework (Trust, Review, Accountability, Critique, Explainability), a governance-first architecture designed to make multi-agent AI systems auditable, policy-aligned, and operationally reliable across varying degrees of agent […]
