Enterprises are knowingly deploying AI agents without adequate controls. They are now working to retrofit these systems and have allocated budgets for vendor changes across five control layers. These layers include agent identity, output evaluation, cost telemetry, context management, and orchestration. Companies are already facing consequences, with a majority experiencing agent security incidents or near-misses. Many also exhibit reactive control over agent spending, only learning costs upon receiving invoices.A significant finding is that 86% of enterprises running their own GPUs report utilization below 50%. Furthermore, only 44% rigorously track AI compute costs and returns, with most still estimating. Many deployed "agents" are basic single-prompt chatbots, not capable of complex multi-step tasks. This highlights a prevalent "agentwashing" trend, where simpler tools are mislabeled as true agents.Two-thirds of enterprises allow AI agents to push changes to production based on automated evaluations, despite only 5% fully trusting these systems. Half of enterprises have shipped an agent that caused a customer-facing failure after passing internal evaluations. A significant 69% of companies permit agent credential sharing, leading to substantially higher rates of security incidents.Fifty-seven percent of enterprises have traced incorrect agent answers to missing or inconsistent business context, such as wrong metrics or stale definitions. The need for AI agent "portability" has emerged as a priority, with enterprises anticipating hybrid orchestration control planes. No single vendor has established dominance in any of the five critical control layers. Enterprises are primarily defaulting to the built-in tools provided by their existing cloud and model providers for guardrails and solutions. Future surveys will track whether these planned budget allocations lead to improved agent security, evaluation rigor, GPU utilization, and semantic layer implementation.