Responsible and Explainable AI Agents with Consensus-Driven Reasoning
Research Paper#AI Agents, Explainable AI, Responsible AI, LLMs, VLMs🔬 Research|Analyzed: Jan 4, 2026 00:15•
Published: Dec 25, 2025 14:49
•1 min read
•ArXivAnalysis
This paper addresses the critical challenges of explainability, accountability, robustness, and governance in agentic AI systems. It proposes a novel architecture that leverages multi-model consensus and a reasoning layer to improve transparency and trust. The focus on practical application and evaluation across real-world workflows makes this research particularly valuable for developers and practitioners.
Key Takeaways
- •Proposes a Responsible and Explainable AI Agent Architecture (RAI/XAI).
- •Employs multi-model consensus to improve robustness and transparency.
- •Uses a dedicated reasoning agent for safety, policy enforcement, and decision making.
- •Focuses on practical application and evaluation in real-world agentic workflows.
Reference / Citation
View Original"The architecture uses a consortium of heterogeneous LLM and VLM agents to generate candidate outputs, a dedicated reasoning agent for consolidation, and explicit cross-model comparison for explainability."