Enterprise AI teams are adopting multiple orchestration platforms, with the median enterprise using three simultaneously, driven by a lack of trust in single vendors for security and permissioning. Microsoft currently leads in primary usage, while Anthropic's Claude Platform is heavily considered for future adoption by enterprises. Key challenges include managing token usage and gaining visibility into agent spending.The ongoing analysis, based on feedback from AI builders, reveals that 85% of enterprises use two or more orchestration tools, with 64% using three, showcasing a deliberate pluralistic approach. Microsoft AI Foundry, OpenAI’s Agents SDK, and Anthropic’s Claude Platform are primary tools, supplemented by custom in-house orchestration for 22% of builders. A significant trend towards hybrid control planes is expected, with over half of respondents anticipating this by 2026.Enterprises are actively planning platform changes, with over two-thirds expecting to switch within a year, and Anthropic's Claude Agent SDK being a top consideration. This multi-platform strategy reflects a desire to avoid vendor lock-in, a lesson learned from early cloud adoption. While overall satisfaction with platforms is high, ease of implementation and value for money receive lower ratings.Buying decisions are primarily influenced by flexibility, security, production reliability, and control over agent execution, rather than model gravity or development ease. Spending priorities reflect these concerns, with significant investments in agent monitoring, debugging, and security enforcement. Enterprises are optimizing for task completion reliability and multi-step workflow management, indicating a focus on core orchestration rather than end-user experience.Major concerns for builders include security and permissioning limitations, vendor lock-in, limited visibility, and inflexibility regarding models and tools. A significant issue is the struggle to control agent token use, with one in five enterprises unable to stop runaway agent spending in real time. Various strategies are employed to manage costs, including native platform controls, custom gateway plumbing, and dynamic routing.Despite these efforts, a quarter of respondents still rely on reactive monitoring without real-time kill switches for runaway agents. The level of fiscal control maturity does not significantly differ based on organization size. Most enterprises are still in the early stages of deploying true multi-step agents, with only a small fraction reporting advanced, largely autonomous systems.A significant portion of deployments remain basic assistants or chatbots, indicating that the widespread adoption of truly agentic AI is still emerging. The data suggests that while enterprises are building the necessary infrastructure for agents, the full realization of agentic AI is yet to come.