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Journals / Journal of AI / 2026 / Issue: 10

Explainable Action Chains in Agentic AI: Enhancing Transparency and Trust

Aybeyan Selim(International Vision University)0000-0001-8285-2175
İlker Ali(International Vision University)0000-0002-2111-415X
Cansur Cafer(International Vision University)0009-0004-9186-2200
Pages
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Publication Date
2026-07-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results demonstrate that XAC significantly increases perceived transparency and user trust without significantly affecting task success, while introducing only a modest increase in decision time.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study addresses the growing challenge of explainability in Agentic Artificial Intelligence (Agentic AI), where autonomous systems operate through sequential, goal-driven decision processes rather than isolated predictions.
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Key Findings & Data:The framework is integrated into a modular Agentic AI architecture following the Goal–Plan–Act–Reflect cycle, enabling explanation generation as an inherent part of the decision process rather than a post-hoc addition.
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Conclusion & Impact:Experimental results demonstrate that XAC significantly increases perceived transparency and user trust without significantly affecting task success, while introducing only a modest increase in decision time.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Agentic AI · Explainable AI · Autonomous Agents

Abstract

This study addresses the growing challenge of explainability in Agentic Artificial Intelligence (Agentic AI), where autonomous systems operate through sequential, goal-driven decision processes rather than isolated predictions. The primary objective of this study is to develop and experimentally evaluate a behavior-centric explainability framework, termed Explainable Action Chains (XAC), for improving transparency and trust in Agentic AI systems. Existing Explainable AI (XAI) approaches are predominantly model-centric and fail to adequately capture the temporal and behavioral complexity of agentic systems. To bridge this gap, we propose Explainable Action Chains (XAC), a behavior-centric framework that represents agent decisions as temporally ordered units linking goals, states, actions, rationales, and outcomes. The framework is integrated into a modular Agentic AI architecture following the Goal–Plan–Act–Reflect cycle, enabling explanation generation as an inherent part of the decision process rather than a post-hoc addition. A prototype implementation is developed and evaluated through a comparative study between a baseline agent and an XAC-enabled agent under identical conditions. Experimental results demonstrate that XAC significantly increases perceived transparency and user trust without significantly affecting task success, while introducing only a modest increase in decision time. These findings validate the feasibility of behavior-level explainability for autonomous AI systems. The proposed framework contributes to the design of more transparent, accountable, and trustworthy Agentic AI systems, and provides a foundation for future research in explainable autonomous decision-making.

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Cite This Article

Selim, A., Ali, İ., Cafer, C. (2026). Explainable Action Chains in Agentic AI: Enhancing Transparency and Trust. Journal of AI, Issue: 10. https://doi.org/10.61969/jai.1955038

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