Agentic Explainability at Scale: Between Corporate Fears and XAI Needs
For enterprise AI governance professionals, this work addresses the practical challenge of scaling agentic AI safely by offering explainability solutions, though it is preliminary and lacks concrete evaluation.
The paper identifies 'Agent Sprawl' as a key risk in enterprise agentic AI adoption, where governance lags behind scaling, and proposes design-time and runtime explainability techniques, including a preliminary Agentic AI Card prototype, to address corporate fears and XAI needs.
As companies enter the race for agentic AI adoption, fears surface around agentic autonomy and its subsequent risks. These fears compound as companies scale their agentic AI adoption with low-code applications, without a comparable scaling in their governance processes and expertise resulting in a phenomenon known as "Agent Sprawl". While shadow AI tools can help with agentic discovery and identification, few observability tools offer insights into the agents' configuration and settings or the decision-making process during agent-to-agent communication and orchestration. This paper explores AI governance professionals' concerns in enterprise settings, while offering design-time and runtime explainability techniques as suggested by AI governance experts for addressing those fears. Finally, we provide a preliminary prototype of an Agentic AI Card that can help companies feel at ease deploying agents at scale.