Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908731979071488 |
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| author | Bandara, Eranga Hewa, Tharaka Gore, Ross Shetty, Sachin Mukkamala, Ravi Foytik, Peter Rahman, Abdul Bouk, Safdar H. Liang, Xueping Hass, Amin Rajapakse, Sachini Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan |
| author_facet | Bandara, Eranga Hewa, Tharaka Gore, Ross Shetty, Sachin Mukkamala, Ravi Foytik, Peter Rahman, Abdul Bouk, Safdar H. Liang, Xueping Hass, Amin Rajapakse, Sachini Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan |
| contents | Agentic AI represents a major shift in how autonomous systems reason, plan, and execute multi-step tasks through the coordination of Large Language Models (LLMs), Vision Language Models (VLMs), tools, and external services. While these systems enable powerful new capabilities, increasing autonomy introduces critical challenges related to explainability, accountability, robustness, and governance, especially when agent outputs influence downstream actions or decisions. Existing agentic AI implementations often emphasize functionality and scalability, yet provide limited mechanisms for understanding decision rationale or enforcing responsibility across agent interactions. This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic workflows based on multi-model consensus and reasoning-layer governance. In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context, explicitly exposing uncertainty, disagreement, and alternative interpretations. A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions. Explainability is achieved through explicit cross-model comparison and preserved intermediate outputs, while responsibility is enforced through centralized reasoning-layer control and agent-level constraints. We evaluate the architecture across multiple real-world agentic AI workflows, demonstrating that consensus-driven reasoning improves robustness, transparency, and operational trust across diverse application domains. This work provides practical guidance for designing agentic AI systems that are autonomous and scalable, yet responsible and explainable by construction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning Bandara, Eranga Hewa, Tharaka Gore, Ross Shetty, Sachin Mukkamala, Ravi Foytik, Peter Rahman, Abdul Bouk, Safdar H. Liang, Xueping Hass, Amin Rajapakse, Sachini Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan Artificial Intelligence Agentic AI represents a major shift in how autonomous systems reason, plan, and execute multi-step tasks through the coordination of Large Language Models (LLMs), Vision Language Models (VLMs), tools, and external services. While these systems enable powerful new capabilities, increasing autonomy introduces critical challenges related to explainability, accountability, robustness, and governance, especially when agent outputs influence downstream actions or decisions. Existing agentic AI implementations often emphasize functionality and scalability, yet provide limited mechanisms for understanding decision rationale or enforcing responsibility across agent interactions. This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic workflows based on multi-model consensus and reasoning-layer governance. In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context, explicitly exposing uncertainty, disagreement, and alternative interpretations. A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions. Explainability is achieved through explicit cross-model comparison and preserved intermediate outputs, while responsibility is enforced through centralized reasoning-layer control and agent-level constraints. We evaluate the architecture across multiple real-world agentic AI workflows, demonstrating that consensus-driven reasoning improves robustness, transparency, and operational trust across diverse application domains. This work provides practical guidance for designing agentic AI systems that are autonomous and scalable, yet responsible and explainable by construction. |
| title | Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.21699 |