Abstract : 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 autonomy. TRACE embeds governance anchors at the agent level, enforces data privacy and policy checks, supplies a dedicated Critic agent for meta-validation, and preserves human-in- the-loop oversight where required. We present a layered architecture that separates Governance & Compliance, Operational Agents, and Oversight & Assurance, and provide a concrete methodology for instrumenting agent behaviour with provenance, explainability outputs, and per-agent metrics. A formal scoring rubric—comprising agent operational metrics, critic checks, and aggregation rules—yields an Overall System Confidence (OSC) that drives automated actions, human escalation, and continuous learning. Finally, we propose a suite of operational KPIs for each layer as Governance and Compliance Indicators (GCI), Agentic Performance Metrics (APM), and Assurance Indicators (AI) that enable financial institutions and other regulated organisations to deploy multi-agent systems that are efficient, auditable, and compliant. TRACE bridges the gap between regulatory expectations and system engineering practice— providing a practical roadmap for trustworthy multi-agent AI deployment in high-stakes domains.
Sinha Dr. Nabanita (2026) studied this question.