Platform engineering for the agentic enterprise: Managing applications, resources, and AI agents

Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond... Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond what individual development teams could reasonably manage. Every application required deployment pipelines, infrastructure provisioning, networking, security, observability, compliance, and operational expertise. While each capability was valuable, expecting every development team to master all of them quickly became unrealistic. Internal Developer Platforms (IDPs) emerged to address this challenge. Rather than asking every team to build its own operational tooling, platform engineering teams created shared platforms that encapsulated best practices behind opinionated workflows, self-service capabilities, standardized environments, and golden paths. These platforms enabled developers to focus on delivering business functionality while the platform consistently handled deployment, security, governance, and operations. Over time, they evolved far beyond deployment automation, integrating continuous delivery, GitOps, observability, policy enforcement, governance, and developer experience into a unified operating model. That model has been enormously successful, but it was built around a fundamental assumption: the primary consumer of the platform is a human developer. Developers use the platform to provision environments, deploy applications, consume resources, and operate software, while the platform abstracts away much of the underlying complexity. The Agentic Enterprise changes that assumption. Increasingly, AI agents are

Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond... Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond what individual development teams could reasonably manage. Every application required deployment pipelines, infrastructure provisioning, networking, security, observability, compliance, and operational expertise. While each capability was valuable, expecting every development team to master all of them quickly became unrealistic. Internal Developer Platforms (IDPs) emerged to address this challenge. Rather than asking every team to build its own operational tooling, platform engineering teams created shared platforms that encapsulated best practices behind opinionated workflows, self-service capabilities, standardized environments, and golden paths. These platforms enabled developers to focus on delivering business functionality while the platform consistently handled deployment, security, governance, and operations. Over time, they evolved far beyond deployment automation, integrating continuous delivery, GitOps, observability, policy enforcement, governance, and developer experience into a unified operating model. That model has been enormously successful, but it was built around a fundamental assumption: the primary consumer of the platform is a human developer. Developers use the platform to provision environments, deploy applications, consume resources, and operate software, while the platform abstracts away much of the underlying complexity. The Agentic Enterprise changes that assumption. Increasingly, AI agents are becoming consumers of the platform alongside human engineers. They provision infrastructure, deploy applications, investigate incidents, analyze telemetry, invoke operational workflows, interact with enterprise resources, and automate tasks that previously required human intervention. Rather than serving only developers, the platform must now serve both humans and intelligent software, each interacting through interfaces appropriate to their capabilities while operating under the same governance model. This shift is about much more than exposing APIs to AI. It fundamentally changes what the platform is responsible for. Instead of acting solely as a developer platform, it becomes the operational foundation through which developers, platform engineers, SREs, and AI agents build, operate, and continuously improve enterprise software. At the same time, the software being managed is evolving as well. Applications remain central to delivering business value, but they are no longer the only software assets the platform understands. Resources become first-class platform objects, and AI agents become first-class software actors that interact with both applications and resources. The next evolution of platform engineering is therefore about extending the existing platform so it can support both humans and AI agents as first-class consumers, enabling them to collaboratively build, operate, and govern applications, resources, and AI agents within a unified operational model. The agentic enterprise Much of today’s industry discussion focuses on AI agents themselves—how to build them, orchestrate them, or connect them to large language models. While these are important developments, they represent only one aspect of a much broader transformation. The Agentic Enterprise is not defined simply by the introduction of AI agents. It represents a fundamental shift in both the way software enterprises operate and the way that software is built and managed. Enterprise software is no longer composed solely of applications. Organizations will continue to rely on APIs, microservices, event-driven systems, web applications, and integration services to deliver business capabilities. Those applications will continue to depend on databases, messaging systems, object storage, AI models, identity providers, APIs, secrets, SaaS services, and countless other shared resources. What changes is that AI agents become active participants within this software ecosystem. They collaborate with applications, consume and manage resources, investigate incidents, coordinate deployments, analyze telemetry, enforce policies, and automate operational workflows. At the same time, the consumers of the platform are evolving. Historically, developer platforms were designed primarily for human users, developers building applications, platform engineers operating infrastructure, and SREs maintaining production systems. In the Agentic Enterprise, AI agents become platform consumers alongside these human users. They interact with the same platform to provision resources, deploy applications, inspect operational state, invoke workflows, and perform tasks on behalf of their human collaborators. This shift has important implications for platform engineering. The platform can no longer assume that every interaction originates from a human. It must provide interfaces designed for both humans and AI agents, while giving each actor a distinct identity, scoped permissions, and a clear audit trail. A developer using a portal, an SRE using a CLI/GitOps, and an AI agent invoking an MCP server may interact with the platform differently, but their actions should be governed through consistent security controls, policy boundaries, and operational guardrails. The Agentic Enterprise therefore changes both what the platform manages and who the platform serves . It must manage applications, resources, and AI agents as first-class software assets, while simultaneously supporting humans and AI a

Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond... Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond what individual development teams could reasonably manage. Every application required deployment pipelines, infrastructure provisioning, networking, security, observability, compliance, and operational expertise. While each capability was valuable, expecting every development team to master all of them quickly became unrealistic. Internal Developer Platforms (IDPs) emerged to address this challenge. Rather than asking every team to build its own operational tooling, platform engineering teams created shared platforms that encapsulated best practices behind opinionated workflows, self-service capabilities, standardized environments, and golden paths. These platforms enabled developers to focus on delivering business functionality while the platform consistently handled deployment, security, governance, and operations. Over time, they evolved far beyond deployment automation, integrating continuous delivery, GitOps, observability, policy enforcement, governance, and developer experience into a unified operating model. That model has been enormously successful, but it was built around a fundamental assumption: the primary consumer of the platform is a human developer. Developers use the platform to provision environments, deploy applications, consume resources, and operate software, while the platform abstracts away much of the underlying complexity. The Agentic Enterprise changes that assumption. Increasingly, AI agents are becoming consumers of the platform alongside human engineers. They provision infrastructure, deploy applications, investigate incidents, analyze telemetry, invoke operational workflows, interact with enterprise resources, and automate tasks that previously required human intervention. Rather than serving only developers, the platform must now serve both humans and intelligent software, each interacting through interfaces appropriate to their capabilities while operating under the same governance model. This shift is about much more than exposing APIs to AI. It fundamentally changes what the platform is responsible for. Instead of acting solely as a developer platform, it becomes the operational foundation through which developers, platform engineers, SREs, and AI agents build, operate, and continuously improve enterprise software. At the same time, the software being managed is evolving as well. Applications remain central to delivering business value, but they are no longer the only software assets the platform understands. Resources become first-class platform objects, and AI agents become first-class software actors that interact with both applications and resources. The next evolution of platform engineering is therefore about extending the existing platform so it can support both humans and AI agents as first-class consumers, enabling them to collaboratively build, operate, and govern applications, resources, and AI agents within a unified operational model. The agentic enterprise Much of today’s industry discussion focuses on AI agents themselves—how to build them, orchestrate them, or connect them to large language models. While these are important developments, they represent only one aspect of a much broader transformation. The Agentic Enterprise is not defined simply by the introduction of AI agents. It represents a fundamental shift in both the way software enterprises operate and the way that software is built and managed. Enterprise software is no longer composed solely of applications. Organizations will continue to rely on APIs, microservices, event-driven systems, web applications, and integration services to deliver business capabilities. Those applications will continue to depend on databases, messaging systems, object storage, AI models, identity providers, APIs, secrets, SaaS services, and countless other shared resources. What changes is that AI agents become active participants within this software ecosystem. They collaborate with applications, consume and manage resources, investigate incidents, coordinate deployments, analyze telemetry, enforce policies, and automate operational workflows. At the same time, the consumers of the platform are evolving. Historically, developer platforms were designed primarily for human users, developers building applications, platform engineers operating infrastructure, and SREs maintaining production systems. In the Agentic Enterprise, AI agents become platform consumers alongside these human users. They interact with the same platform to provision resources, deploy applications, inspect operational state, invoke workflows, and perform tasks on behalf of their human collaborators. This shift has important implications for platform engineering. The platform can no longer assume that every interaction originates from a human. It must provide interfaces designed for both humans and AI agents, while giving each actor a distinct identity, scoped permissions, and a clear audit trail. A developer using a portal, an SRE using a CLI/GitOps, and an AI agent invoking an MCP server may interact with the platform differently, but their actions should be governed through consistent security controls, policy boundaries, and operational guardrails. The Agentic Enterprise therefore changes both what the platform manages and who the platform serves . It must manage applications, resources, and AI agents as first-class software assets, while simultaneously supporting humans and AI a