The next big thing in the platform engineering ecosystem as they say is AI Agents for Platform Engineering. How to build them, how to run them on Kubernetes, best practices, the journey from microservices to microagents, leveraging agent frameworks in platforms, MCP servers on Kubernetes, Performance Optimization with Kubernetes: DRA, and so much around AIOps for Platforms.
While AI agents and next-gen tooling generate the loudest buzz in platform engineering circles today, this blog aims to spotlight something more foundational: the discipline of platform engineering itself and why it deserves continued attention from the community.
I was at KubeCon + CloudNativeCon London around a couple of months ago and beyond all the chatter around how Kubernetes is driving breakthrough LLMs & developing Agentic AI models on clusters, there was chatter around this one topic that has been around for a decent amount of time but hasn’t found mainstream adoption as a practice in spite of always being a crucial part of the DevOps practice.
In the ever-evolving world of software delivery, we often cherish fancy frontends, breakthrough AI models, and lightning-fast APIs. A complete software delivery solution is seen as a mix of managing your CI/CD, managing your Infrastructure as Code, better Feature flagging, robust resiliency practices, managing cloud costs and finding software engineering insights. But behind all of this innovation lies a quiet, powerful force, the term popularly known as Platform Engineering.
Although many may not see it, you’ll definitely feel its impact — every time a developer spins up an environment in seconds, deploys code with confidence, or recovers from failure without breaking a sweat.
So, what is this invisible backbone that’s reshaping the software lifecycle?
Platform Engineering, Defined
In my view, platform engineering is the discipline of designing, building, and maintaining internal developer platforms (IDPs) — self-service layers that abstract infrastructure complexity and empower developers to deliver software faster, more securely, and with less friction.
Think of an IDP as a developer’s cockpit: pre-configured with everything they need — CI/CD pipelines, observability, security gates, provisioning tools — without requiring deep knowledge of the underlying systems.
But building an IDP isn’t just about integrating tools. It’s about crafting an experience where infrastructure becomes a product developers enjoy using.
Platform Democracy
Platform engineering has evolved through multiple stages—from the rigid separation of Development and Operations, to the emergence of DevOps, and eventually to centralized Platform Teams. While Platform Teams aimed to streamline developer workflows, they often became bottlenecks as demand scaled.
To address this, the concept of a Platform Group emerged, emphasizing collaboration across multiple teams with varying focus areas—from infrastructure to developer enablement. However, even in this model, traditional roles of “producers” and “consumers” can lead to inefficiencies and rigid boundaries.
I was reading this blog, “Platform Democracy: Rethinking Who Builds and Consumes Your Internal Platform” by Colin Humphreys (Syntasso) on the CNCF blog recently,
And I can conclude the need for:
Platform Democracy: a model where all stakeholders—developers, SREs, security teams, and external providers—participate equally in producing and consuming platform capabilities. This shifts the model from centralized ownership to distributed collaboration.
Redefining Roles
- Producers are no longer just the platform team; they include internal teams, external service providers, and compliance engineers.
- Consumers expect the platform to be fast, safe, and efficient, enabling self-service without introducing risks or cognitive overload.
Key Enablers of Platform Democracy
- Promises: Contract-based provisioning to blend, buy, or build services.
- Plugins: Lightweight, flexible governance enforcement.
- Fleet Upgrades: Non-disruptive updates across platform components.
Why Platform Engineering Now?
The shift to cloud-native architecture and Kubernetes has made infrastructure more flexible, but also more complicated. Teams often end up juggling YAML, Helm charts, Terraform scripts, and CI/CD configurations — just to ship a single microservice.
Platform engineering emerged as a solution to this complexity. By providing golden paths, self-service infrastructure, and standardized workflows, it allows organizations to scale development without scaling chaos.
The Role of Internal Developer Platforms (IDPs)
At the heart of platform engineering is the IDP. These platforms are opinionated, curated, and customized to match the needs of engineering teams. A good IDP offers:
- Self-service provisioning for environments, databases, and services
- Integrated observability for real-time insights and troubleshooting
- Secure workflows that automate compliance and policy enforcement
- Templates and golden paths to speed up new project onboarding
- Consistency across teams, regardless of cloud provider or deployment strategy
An IDP doesn’t remove flexibility — it enables velocity without compromising control.
Meet k0rdent: Orchestrating Kubernetes Platforms, the Smart Way
To bring these concepts to life, let’s talk about k0rdent, a Kubernetes-native Distributed Container Management Engine (DCME) designed to streamline platform operations across complex environments.
In my belief, k0rdent is not just a tool—it’s an enabler, in the platform engineering ecosystem. It is a powerful enabler of IDPs while using Kubernetes as the one source of truth.
k0rdent acts as a “super control plane”, abstracting away the pain points of multi-cluster lifecycle management. Built on ClusterAPI, Sveltos, and declarative templates, k0rdent enables platform teams to:
- Provision and manage AI/ML clusters at scale
- Automate policy enforcement across cloud and hybrid environments
- Apply GitOps-driven workflows for consistent, auditable deployments
- Detect anomalies and trigger auto-scaling using built-in observability hooks
- Power self-service environments within internal developer platforms
In many ways, k0rdent isn’t just a tool — it’s an enabler for building intelligent, scalable, and secure IDPs on top of Kubernetes.
From Tooling to Experience: The Future of Platform Engineering
Platform engineering is no longer just about infrastructure. It’s about developer experience. And that experience is increasingly being shaped by how well platforms integrate AI/ML, handle multi-cloud workloads, and respond to dynamic scaling needs.
We believe the future lies in smart platforms — those that adapt, learn, and support developers without getting in their way. Projects like k0rdent are part of this new wave, bringing intelligence to the infrastructure layer.
AI is emerging as a critical layer in modern platform engineering—not just as an application feature, but as a governed, integrated capability within internal platforms. Simply calling an LLM from an app bypasses the platform’s core value of consistency, governance, and shared services.
In one of the talks at Platform Engineering Day (KubeCon + CloudNativeCon Co-Located event)
Mauricio Salatino addressed how the Dapr AI Agent Framework (an extension of the Dapr project) was introduced to help build autonomous, resilient, and observable AI agents. This framework emphasizes core platform concerns such as workflow orchestration, security, state management, and telemetry.
Key crosscutting concerns AI integrations must uphold in platform environments include:
- Security: Unified access controls, secure credential handling, and data obfuscation.
- Resiliency: Retry logic, timeouts, and circuit-breaking.
- Observability: Full tracing, metrics, and logging.
Salatino stressed that platform teams must treat LLM access like any other infrastructure component, integrating it with company-wide policies and controls. The goal is a consistent, secure, and maintainable AI experience across teams and languages.
Finally, as with all cloud-native efforts, community collaboration is essential. Platforms should not be built in silos—knowledge sharing across companies is key to evolving platform engineering with AI.
Final Thoughts
In a world where software velocity is everything, platform engineering has become the quiet hero behind high-performing teams. Internal developer platforms are the product, and teams like yours are the engineers behind the curtain.
Whether you’re a startup racing to iterate or an enterprise modernizing legacy systems, the lesson is clear: invest in your platform team. Give your developers the tools and experiences they deserve — and watch your engineering velocity transform.