Claude’s next enterprise battle is not models: it’s the agent control plane

· Source: VentureBeat · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

New VB Pulse survey data from February 2026 indicates a strategic shift in enterprise AI, moving beyond model quality to focus on agent orchestration infrastructure. Microsoft Copilot Studio and Azure AI Studio lead with 38.6% primary-platform adoption, up from 35.7% in January, while OpenAI's Assistants and Responses API hold second place at 25.7%, increasing from 23.2%. Notably, Anthropic made its first appearance in the tracker, rising from 0% to 5.7% in February for tool use and workflows, suggesting its model momentum is spilling into the orchestration layer. The survey highlights that security and permissions are the top selection criteria for orchestration platforms, at 37.1%, with vendor lock-in emerging as a significant concern, increasing from 23.2% to 25.7%. Enterprises are increasingly adopting multi-model strategies but seek unified control planes for governance and auditability.

Key takeaway

For AI Product Managers and VPs of Engineering evaluating agent orchestration platforms, recognize that the market is prioritizing control, security, and governance over raw model performance. Your teams should focus on solutions that offer robust identity integration, audit logs, and sandboxing capabilities, while actively planning for a multi-vendor, hybrid control plane to mitigate vendor lock-in risks. This strategic shift means infrastructure decisions for agents are becoming as critical as cloud infrastructure choices.

Key insights

The enterprise AI battle is shifting from model quality to control over agent orchestration infrastructure, with security and governance as key drivers.

Principles

Method

Enterprises are moving from LLMOps to "Agent Ops," requiring governance that covers the entire agent lifecycle, including identity, access controls, and policy separation from agent logic.

In practice

Topics

Best for: VP of Engineering/Data, AI Product Manager, Investor, Director of AI/ML, AI Architect, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by VentureBeat.