The Agent Control Plane War: Why Anthropic's 5.7% Foothold Signals a Strategic Shift

The enterprise AI narrative has centered on model performance for the past two years—GPT versus Claude versus Gemini in an endless benchmark battle. But new survey data suggests the real strategic fight is shifting to a different layer entirely: who controls the infrastructure where AI agents actually run.

According to VB Pulse survey data tracking enterprise agentic orchestration preferences, Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January. OpenAI’s Assistants and Responses API held second place at 25.7%, rising from 23.2%. The more strategically interesting development: Anthropic moved from 0% in January to 5.7% in February for tool use and workflows—its first appearance in the orchestration tracker.

The absolute number is small—four respondents out of 70 in this cohort. But the emergence marks something significant: Claude usage moving from the model layer into native orchestration, where the operational machinery of AI work actually lives.

The Control Plane Is the New Battleground

The distinction between model selection and orchestration platform selection matters enormously for enterprise architecture decisions. As Tom Findling, CEO and cofounder of AI cybersecurity startup Conifers, stated: “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it.”

This control plane encompasses where agents plan, call tools, access data, run workflows, and—critically for enterprise adoption—prove to security teams that they operated within authorized boundaries. Enterprises are deciding whether this operational layer will sit inside Microsoft’s stack, OpenAI’s API layer, Anthropic’s managed runtime, an open framework, or some hybrid combination.

The competitive dynamics here differ fundamentally from model competition. A model is relatively easy to swap—companies can route different workloads to different providers based on task requirements, cost, and risk profile. The VB Pulse Foundation Models tracker shows multi-model strategy is already the enterprise consensus, with respondents increasingly reporting adoption of multiple models with orchestration layers routing across them.

An agent runtime is different. Once workflows, tool permissions, credentials, audit logs, memory, sandboxed execution, and operational monitoring live inside one provider’s environment, switching becomes less like changing models and more like changing infrastructure.

Anthropic’s Model Momentum May Be Spilling Over

The orchestration foothold becomes more interesting when viewed alongside Anthropic’s model-layer trajectory. According to VB Pulse Q1 Foundation Models and Intelligence Platforms tracker data, Anthropic rose from 23.9% in January to 28.6% in February, then to 56.2% in March among qualified enterprise respondents. The March reading carries a significant caveat: the sample was only 16 respondents, flagged as directional only.

The pattern suggests Anthropic’s model adoption may be starting to pull enterprises toward its orchestration capabilities. This is the strategic scenario that should concern Microsoft and OpenAI—not that Anthropic has captured orchestration today, but that model preference could translate into infrastructure lock-in over time.

Anthropic has signaled its intentions clearly. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing and built-in tool capabilities. The company appears to be positioning itself not just as a model provider but as an operational platform.

The Governance and Security Dimension

Findling’s statement highlights another critical factor: “In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”

This governance layer may ultimately determine enterprise platform selection more than model performance. Related VB Pulse surveys paint a concerning picture of current enterprise AI governance: one survey found 72% of enterprises don’t have the control and security they think they do, while another reported 88% of enterprises experienced AI agent security incidents last year.

These findings suggest enterprises are actively seeking orchestration platforms that can address governance gaps. The provider that solves auditability, permission management, and security monitoring most effectively may capture disproportionate market share regardless of underlying model quality.

The uncertainty here is significant: it remains unclear whether enterprises will consolidate on single orchestration platforms or maintain hybrid approaches across multiple providers. The switching costs of agent runtimes suggest consolidation pressure, but enterprise risk management often favors avoiding single-vendor dependency.

What This Means for SaaS Teams

For SaaS operators building AI-powered features, the orchestration layer competition has immediate implications:

Platform selection is becoming infrastructure selection. Choosing an agent orchestration platform today may constrain options for years. Teams should evaluate not just current capabilities but the strategic trajectory of each provider’s orchestration roadmap.

Governance capabilities matter for enterprise sales. If your product uses AI agents, enterprise buyers will increasingly ask where those agents run and how they’re audited. Building on platforms with strong governance features—or building those features yourself—becomes a competitive requirement.

Multi-model flexibility may require orchestration independence. If you want to route between Claude, GPT, and other models based on task requirements, you may need orchestration infrastructure that isn’t tightly coupled to any single model provider.

Watch the 5.7% number. Anthropic’s small foothold in orchestration is worth monitoring precisely because it appeared at the start of a new market structure. If it grows alongside Claude’s model adoption, it signals that model preference is translating into infrastructure lock-in—a pattern that could repeat with other providers.

The enterprise AI competition is entering a new phase. Model quality still matters, but the fight for the control plane—where agents actually operate—may determine which providers capture durable enterprise relationships. For SaaS teams, understanding this shift is essential for making infrastructure decisions that won’t require painful unwinding in two years.