Why AI Agent Retry Logic Deserves Its Own Budget Line

A software engineering post on DEV Community argues that developers building LLM-based agents routinely underestimate costs by budgeting only for successful API calls. The author explains that each retry attempt consumes additional tokens, rate-limit quota, connection slots, and wall-clock time — none of which are free even when an endpoint carries no sticker price. Failed calls in looping agents compound these costs multiplicatively, a phenomenon the author calls the 'retry tax.' To make the problem concrete, the post includes a Python script that calculates expected token usage and wait time across a configurable retry policy using user-supplied failure-rate and latency figures. The core recommendation is to measure actual failure paths from production traces and price them explicitly rather than relying on best-case estimates.