Articles

Building Trust in Agentic AI: TRACE Framework for Policy-Driven Multi-Agent System Design

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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.

Avoiding Ambiguous Framework in Distance-based Formation Control Using Virtual Followers

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In this study, we develop a shape formation controller based on regular distance-based control law. As distance rigidity theory may not be successful in achieving desired shape when the control system defined under minimally rigid graph. As a matter of fact, the formation may converge to ambiguous framework when the group of mobile robots did not initialize in proper way. This is a well-known issue with distance-based formation control while there are multiple equilibrium points in the dynamics. We introduce a new controller by adding virtual constraints to the system. The performance of control system is exemplified through numerical simulations and the convergence to the desired formation for all initial conditions validated with stability analysis.