Platform engineers provide capabilities to internal developers, and they build interfaces to those capabilities that meet developers where they are already working, be it web portal, CLI, or API. But lately, "where developers are already working" is alongside their AI coding assistant. Developers want to be able to ask the platform natural language questions like "Why won't my application deploy?" and "What types of databases are available to me?," and receive helpful answers. Developers want their coding assistant to investigate the platform's underlying Kubernetes cluster, surface the problem, and then apply fixes or recommend capabilities. This session is a live demo of exactly that: an AI agent that a coding assistant can use to interface with an internal platform on the developer's behalf. It is built from scratch with LangGraph, a vector database, and OpenTelemetry instrumentation. It is available to the coding assistant via MCP and CLI interfaces. LangGraph handles the Kubernetes cluster investigation tools. Then the vector database makes the cluster's capabilities searchable by natural language query. Finally, the traces help platform engineers understand how developers are using the agent: every LLM call, every tool execution, every reasoning step visible in an observability backend. This talk will show you why custom AI agents are wildly useful as a platform interface, and how to start building one at your organization.
Whitney Lee is a creator and systems thinker who explores how observability, AI, and platform engineering connect across the cloud native ecosystem. She brings humor, depth, and clarity to complex technologies while building original frameworks that help others understand how systems fit together. She runs a vibrant YouTube channel, hosts Datadog Illuminated and Software Defined Interviews, has delivered two KubeCon keynotes and countless breakout talks, and combines storytelling and technical rigor to illuminate the human side of cloud native engineering.
