https://video.ut0pia.org/videos/watch/dabe06f0-b909-464f-84aa-9a95bd26b22d You shipped a REST API. Then an SDK. Then MCP tool calling. And still, when a developer asks a coding agent to use your platform, it invents steps, and breaks in production. The problem is that your platform isn't teachable yet. The fix is a skill layer. Versioned, task-specific packages that encode the workflow knowledge your platform team has always had. Building step ordering, failure modes and security hygiene into something agents can load and execute. This became one of the hardest challenges we have seen on building enterprise agentic AI platform for Fortune 50. The talk walks through fundamentals of platform agent skills as a live case study: ML predictions, model training, deployment, monitoring, CI/CD, and agent observability - packaged as installable skills that work across Claude Code, Cursor, Codex. This pattern will paint a clear design pattern for building your own agent-native platform layer, and one question answered: what does it actually take to make your platform teachable? Speakers: Elvin Aghammadzada (DataRobot): Elvin Aghammadzada is a data science engineer on DataRobot's Agent Workforce Platform, focused on bringing production-grade agentic AI to the enterprise. LinkedIn: https://www.linkedin.com/in/elvin-agammed/ GitHub: https://github.com/elvinagam Tue, 21 Jul 2026 19:18:31 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/dabe06f0-b909-464f-84aa-9a95bd26b22d All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.