AI Agent Resilience: A New Framework for Safer Code Fixes

A developer proposes a structural resilience framework for AI agents that apply code fixes, run tests, and commit or roll back changes. The approach uses architectural annotations to prevent system collapse during automated code generation and execution. Two implementations are compared: a standard imperative version and a structured version with explicit resilience markers.