arXiv:2607.03386v1 Announce Type: new Abstract: Agentic AI systems are increasingly used to edit, refine, and repair decision policies, but evaluating these edits is difficult when per-state expert action labels are unavailable. We study this problem in a hotel-pricing simulator where an agentic policy editor receives only region-level diagnostic feedback: summaries of how its price distribution differs from a benchmark policy across time, inventory, and market regions. The editor cannot observe benchmark actions, benchmark source code, reward numbers, or held-out outcomes, and may only propos
The proliferation of agentic AI systems necessitates robust evaluation methods as these systems mature and are deployed in complex, opaque decision-making environments.
This research addresses a critical limitation in AI deployment: the difficulty of auditing and repairing AI policies when full transparency into expert actions or ground truth is unavailable.
The ability to audit and improve AI decision-making policies without direct access to expert-level actions or internal metrics, relying instead on high-level aggregate feedback.
- · Businesses deploying opaque AI systems
- · AI developers focused on explainability and robustness
- · Auditors and regulators of AI systems
- · Traditional AI auditing methodologies
AI systems gain the capacity for more autonomous self-correction and refinement based on indirect feedback.
Increased adoption of agentic AI in sensitive domains where expert action labeling is impractical or impossible.
New regulatory frameworks may emerge to certify AI auditability based on aggregate feedback paradigms.
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Read at arXiv cs.AI