Approval Fatigue

Approval fatigue refers to the exhaustion and decision-making degradation that occurs when reviewers must evaluate an excessive number of AI model deployments, policy changes, or compliance assessments in succession. In enterprise governance, this phenomenon creates dangerous blind spots because fatigued approvers become more likely to rubber-stamp requests without proper scrutiny, inadvertently weakening control mechanisms and increasing the risk of deploying non-compliant or harmful AI systems. Organizations addressing approval fatigue typically redesign their governance workflows to include automated pre-screening, risk-tiered review processes, and clearer escalation criteria that reserve human review for genuinely high-stakes decisions.

1 item

ResearchGlobal2026-08-06

One in Three Dangerous Agent Requests Bypasses Human Review, Research Finds

A large-scale simulation study, corroborated by Anthropic telemetry from Claude Code, found that human reviewers failed to block roughly one in three malicious AI agent requests, with credential-exfiltration attempts missed 35 percent of the time. Approval rates reached 93 percent across all permission prompts, with reviewer diligence declining as prompt volume increased. The findings directly challenge human-in-the-loop controls as a sufficient primary safeguard for enterprise agentic AI deployments.

agentic AI human-in-the-loop Claude Code Anthropic AI safety controls credential exfiltration approval fatigue agentic coding assistants