AI tools already run inside your organization: MCP servers over internal docs, RAG services over company data, and agents that call other systems. Agents use the same platform APIs your developers do, but they need different things: a finite context budget, no memory between sessions unless the platform keeps it, an identity of their own, and a human before they write. Without shared guardrails, every team solves isolation, data access, and policy on its own, and the platform team becomes a ticket queue. This talk applies platform engineering to AI tools: paved roads where a team brings its tool and the platform provides an isolated runtime with guardrails in place from the start. We cover: - Roads by risk class: a read-only docs server, a data tool, an agent with write access, and a GPU workload each get a different baseline - Isolating teams on a shared cluster with virtual clusters - A live demo: a team ships a real MCP server without writing a security rule, the road refuses an unsafe deployment, and another team's AI assistant uses the tool from a plain-English request - A maturity model for agent platforms, from ad hoc API keys to agents that earn wider permissions.
An active contributor to open source, content creator on Medium and YouTube, focusing on practical, scalable solutions in cloud-native environments. DevOps and Platform Engineering practitioner and advocate.
