Platform Engineering 2.0: Managing AI Costs and Risks Without Rebuilding Infrastructure

The rapid integration of artificial intelligence into enterprise operations has presented platform engineering teams with a formidable new set of challenges, fundamentally altering the assumptions that underpinned traditional software delivery. While platform engineering has successfully established robust foundations through standardized Kubernetes clusters, CI/CD pipelines, internal developer platforms (IDPs), and self-service infrastructure, the arrival of AI workloads demands a more sophisticated and adaptive framework. The conventional debate between DevOps and platform engineering now seems secondary to the pressing need to govern, isolate, and operate AI applications on infrastructure that was not originally designed for such demands. In response to this evolving landscape, Broadcom and PlatformEngineering.org have unveiled the "Platform Engineering 2.0" framework. This initiative is not a call for a complete architectural reset but rather a strategic evolution that leverages and extends the successful principles of Platform Engineering 1.0. The core objective is to enable organizations to manage the complexities, costs, and inherent risks associated with AI without necessitating a complete rebuild of their existing infrastructure. A cornerstone of Platform Engineering 2.0 is the concept of "Model Governance as a Control Plane." This involves establishing a central model registry and a routing layer that unifies authentication, enforces policies across various AI models (whether OpenAI, Anthropic, or on-premises solutions), and provides a single pane of glass for auditing, observability, and compliance. Developers interact with the platform to request model access, and the platform intelligently maps these requests to appropriate risk tiers and approval workflows, effectively transforming the platform into an "infrastructure as AI" system that acts as both a rulebook and a referee for AI operations. Furthermore, the framework acknowledges the emergence of autonomous AI agents as a distinct and critical new class of platform user. These agents, unlike human developers, do not inherit existing personas and operate without human intervention to catch misconfigurations. Consequently, Platform Engineering 2.0 mandates that the platform must seamlessly support both human developers and AI agents as first-class consumers. This shift signifies that platform engineering is no longer solely a software delivery discipline but is rapidly becoming the operational bedrock for an enterprise's "agentic future." The article underscores the urgency of this transition, warning that organizations failing to integrate agentic AI directly into their platform control plane risk significant setbacks in terms of security exposure, operational fragmentation, and uncontrolled AI expenditure.