Making Platforms Teachable for Agents
AI Engineer · youtube · 2026-07-20
DataRobot's presentation argues that even with REST APIs, SDKs, and MCP tool calling, developers using coding agents on a platform might still invent their own steps and cause production errors because the platform isn't "teachable" enough.
Their proposed solution is a skill layer: encapsulating platform knowledge into versionable, task-specific skill packages that agents can directly load and execute. Skills explicitly encode:
- Step sequences
- Failure modes
- Safety guidelines
- Workflow knowledge originally held by the platform team
Using a data science platform scenario, the talk demonstrated making model training, deployment, monitoring, CI/CD, and agent observability capabilities into installable skills usable in environments like Claude Code, Cursor, and Codex. The core goal is to make the platform "teachable" rather than just giving the agent an interface to call.
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