From AI Pilots to Production: Why Governance Now Leads Infrastructure
Enterprises rushing to adopt generative and agentic AI are discovering that models are the easy part; control is not. As AI touches more business-critical workflows, leaders must align AI infrastructure governance with security, compliance and operational resilience. Only a minority of organizations currently feel ready to support large-scale AI workloads, underscoring the gap between experimentation and production. The emerging response is a new layer of enterprise AI orchestration that unifies agents, models, data pipelines and APIs across clouds. Instead of isolated pilots, enterprises now need consistent policies, observability and access controls that span private AI deployments, edge locations and public cloud services. This shift is redefining AI infrastructure as a governed fabric rather than a collection of tools, making API governance platforms, AI gateways and hybrid cloud AI management core to enterprise AI strategy.
Unisys–Rafay: Orchestrating AI Across Hybrid and Regulated Environments
The partnership between Unisys and Rafay Systems targets enterprises struggling to scale AI in hybrid and regulated environments. By combining Unisys’ AI expertise and managed cloud services with Rafay’s self-service orchestration platform, the companies are creating a unified AI software layer spanning agents, models and modular AI infrastructure. Delivered as a SaaS model, this layer is designed to bring consistency and security to diverse AI deployments, helping organizations move from proofs of concept to governed production workloads. A key focus is hybrid cloud AI management: orchestrating Kubernetes clusters, GPU-intensive workloads and private AI deployments across on-premises, edge and public clouds with integrated governance. Rafay’s enterprise-grade metering, AI token pricing support and access control help enterprises gain financial visibility and enforce policies at scale. In effect, infrastructure orchestration is converging with AI infrastructure governance to give regulated enterprises more predictable control over complex AI estates.
Persistent–Kong: Building the Control Layer for API-Driven AI
Persistent Systems and Kong are attacking a different but related challenge: how to govern the rapidly converging mesh of APIs, data pipelines, models and agents. As AI moves into production, the bottleneck is less about model access and more about securing and standardizing how systems connect. Their partnership positions Kong’s unified API and AI connectivity platform, including its AI Gateway, as the control layer that sits across APIs, data and AI services. Persistent adds engineering-led delivery and its own GenAI Hub to modernize legacy API environments and operationalize generative and agentic workflows. Policy-driven controls such as PII protection, centralized access management and end-to-end observability bring enterprise-grade security to AI interactions. This approach reinforces API governance platforms as foundational components of enterprise AI orchestration, particularly for organizations operating high-performance workloads across hybrid and multi-cloud AI deployment footprints.
Sensedia’s AI Gateway and the Rise of Agentic Control Fabrics
Sensedia’s general release of its independent, multi-protocol AI Gateway highlights how infrastructure platforms are evolving to provide native AI gateway and governance capabilities. With autonomous agents already operating at machine speed across legacy systems, enterprises face a visibility and control crisis often described as “Shadow AI.” Sensedia positions its AI Gateway between agents and enterprise systems, enforcing governance policies at the point of action and providing a unified view of guardrails and costs. The platform can govern any agent, route across any model, and connect to any system or cloud, supporting architectures based on the Model Context Protocol. Real-world deployments in manufacturing and telecom show how organizations can gain token-level cost observability, shorten security approval cycles and safely expose sensitive systems as governed tools. As Gartner now expects AI gateways to be standard in larger security and AI platforms, such control fabrics are becoming essential to multi-cloud AI deployment.
Toward Unified AI Infrastructure Governance in Multi-Cloud Enterprises
Taken together, these partnerships signal a clear direction for enterprise AI: infrastructure and governance are converging into unified, multi-cloud control planes. Unisys and Rafay are embedding governance into the orchestration of GPUs, Kubernetes and hybrid deployments; Persistent and Kong are treating API and AI connectivity as a single control problem; Sensedia is inserting an independent AI Gateway between agents and critical systems. All three approaches tackle the same core issues—visibility, policy enforcement, cost control and secure connectivity across heterogeneous environments. For enterprises, the implication is that AI infrastructure governance can no longer be an afterthought layered atop disparate tools. Instead, organizations will increasingly adopt platforms that natively blend enterprise AI orchestration, API governance platforms and hybrid cloud AI management. In the agentic era, the winning architectures will be those that make governance and connectivity first-class citizens of AI infrastructure.