arXiv:2605.06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control. Agents may skip required tool calls, invoke tools unnecessarily, or take actions whose consequence becomes visible only after execution. Existing observability methods are external: prompts reveal correlations, evaluations score outputs, and logs arrive only after the model has already acted. In long-horizon settings, these failures are costly because an early tool mistake can
The paper identifies interpretability as a critical barrier to deploying AI agents in high-stakes enterprise workflows, highlighting ongoing efforts to address fundamental limitations.
Dependable and interpretable AI agent tool use is crucial for widespread adoption in sensitive and critical applications, impacting efficiency and trust in automated systems.
Improved interpretability methods could lead to more robust and auditable AI agents, accelerating their integration into enterprise operations and reducing deployment risks.
- · AI agent developers
- · Enterprise AI adopters
- · Auditing and compliance sectors
- · Companies with opaque AI systems
- · Manual workflow providers
Enterprises gain confidence in deploying AI agents for complex tasks.
Increased automation across industries leads to significant productivity gains and workforce re-skilling needs.
The legal and regulatory frameworks for AI accountability evolve to incorporate advanced interpretability standards, influencing future AI development.
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Read at arXiv cs.AI