Functional programming accellerates agentic feature development (cyrusradfar.com) AI

The article argues that most AI agent failures in production stem from codebase architecture—especially mutable state, hidden dependencies, and side effects—rather than model capability. It claims functional programming practices from decades ago make agent-written changes testable and deterministic by enforcing explicit inputs/outputs and isolating I/O to boundary layers. Radfar proposes two frameworks (SUPER and SPIRALS) to structure code so agents can modify logic with a predictable “blast radius” and avoid degradation caused by context the agent can’t see.