Four Production Gaps Blocking AI Agent Budget Approvals, Says Infrastructure CEO

An AI infrastructure company leader says many teams have working agent prototypes but struggle to secure budget approval due to unresolved production-readiness gaps. The core issues identified are unpredictable costs, lack of auditable execution records, inability to recover in-flight tasks during deployments, and tight coupling to specific frameworks. Finance teams are said to reject proposals not because of model choices but because agent failures can silently inflate token costs in ways that are hard to explain. Regulatory requirements such as the EU AI Act further demand signed, step-by-step execution logs rather than vague references to agent decisions. Addressing these four gaps — cost predictability, evidence trails, task recovery, and framework independence — is presented as the key to unlocking production deployment budgets.