Driving the Agent Quality Flywheel from Your Coding Agent

Building and maintaining high-quality software agents requires a disciplined approach that bridges the gap between anecdotal success and consistent performance in production. This methodology, discussed at Cloud Next '26, is encapsulated in a three-phase flywheel: Build & Test, Ship & Monitor, and Learn & Refine, and further enhanced by a developer-facing path known as the quality-flywheel skill. This skill integrates automated evaluation processes with Google's AutoRaters in collaboration with Google DeepMind, allowing for continuous improvement of agents by conducting targeted testing, analyzing failures, and proposing optimizations without human-in-the-loop grading. The system is designed to identify and rectify subtle failures that might not be immediately obvious, such as discrepancies between an agent's internal state and its output to users, by using custom rubrics and synthetic scenarios to simulate user interactions. As agents mature, the focus shifts from simulated to real production data to ensure that each user interaction serves as a benchmark for further refinement. The quality-flywheel skill is adaptable, serving both specific goals and broader diagnostic purposes, ultimately aiming to create an environment where agents are continuously improvable rather than perfect.

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