Units are Types: Let's Treat Then That Way
(20 Minutes)
By: Emmanuel I. Obi
Experience Level: Intermediate
Slides Link

At SREcon 2026, I showed how untyped infrastructure configs fail silently and how dimensional analysis catches those failures at definition time. But catching errors in human-written configs is only half the story. AI agents are now generating configs, writing IaC, computing dosages, and scaling parameters autonomously. They make the same unit mistakes humans do, faster and more confidently.

This talk extends the SREcon argument into agentic territory. I’ll show how ucon’s MCP server turns dimensional analysis into a verification primitive that any AI agent can call not as a unit converter, but as an algebraic safety net. I'll walk through real agent workflows where structured error responses (dimensional vectors, got/expected pairs, likely-fix hints) enable a model to self-correct in a single retry, and demonstrate how kind-of-quantity enforcement catches errors that no existing unit library can even represent. The core claim: small models paired with algebraic verification outperform large models without it and the infrastructure to prove that should be a tool call, not a training objective.