Intention-aware policy graphs: answering what, how, and why in opaque agents

Agents require explainability to be trustworthy: The paper identifies that AI agents operating in complex environments can exhibit unpredictable "emergent behavior," making it difficult to understand why they act as they do. The authors argue that explaining this behavior is essential for deploying trustworthy AI systems.

A new framework answers three critical questions about agent behavior: The proposed "Intention-aware policy graphs" model enables stakeholders to ask and answer three key questions: "What does the agent want to do?" (its goals), "How does it plan to achieve them?" (its strategy), and "Why is it taking this specific action?" (the reasoning). This structured approach makes opaque agents more interpretable.

Explanations can be measured for quality and reliability: Rather than treating explanations as subjective, the paper contributes specific measurements to evaluate whether the explanations provided by the model are actually interpretable and reliable—addressing a gap in how we assess whether AI explanations are genuinely useful.

The framework works with incomplete information: The model can be constructed from partial observations of an agent's actions and world states, meaning organizations don't need perfect data to begin understanding their AI systems' behavior.

The approach identifies irrational agent behavior: The iterative workflow can flag when agents are behaving irrationally or inconsistently with their stated goals, providing a mechanism for quality assurance and continuous improvement of AI agent trustworthiness.