Your coding agent doesn't always follow your rules — Talha Sheikh, Checkout.com

Reliability in AI coding agents depends less on raw model capability and more on the implementation of deterministic verification layers. While frontier models demonstrate impressive task completion, they frequently fail at specific requirements, necessitating an enforcement harness to validate outputs. The "Vector" project illustrates this by using automated hooks to check agent results against defined configurations, creating a feedback loop that forces retries until the code meets the desired specification. This shift toward "harness engineering"—seen in patterns like Anthropic’s Executive Advisor and various PR review tools—highlights that the true value in AI development lies in designing robust verification systems rather than simply prompting for code. Ultimately, developers must prioritize building these guardrails to ensure trust, as the ability to verify code is becoming more critical than the ability to generate it.