Measure a Managed Agent's Behavior Against Fixed Scenarios Before It Reaches Production
The public-preview Managed Agents run autonomously inside an isolated sandbox, so a small prompt or config change can quietly shift their behavior. Diffing the output once, the way you would for a single prompt, is not enough. Here is how to build a regression harness that runs fixed scenarios repeatedly and judges on pass rate, plus a shadow to canary to full promotion with automatic rollback, all with runnable Python.
Your Managed Agents Bill Has a Second Axis: Drawing a Budget Boundary Around Sandbox Runtime
Managed Agents in public preview bills for tokens and for how long its Google-hosted sandbox stays alive. A single hung run quietly drains your budget on that second axis. Here is a working Python design for wall-clock caps, idle teardown, and a concurrency ceiling.
Before You Let a Managed Agent Ship: Designing Your Own Acceptance Gate
Let the public-preview Managed Agents generate files and broken artifacts will flow straight into production. Here is how to build a verification gate that artifacts must pass before you accept them, with runnable Python and a rejection-feedback loop.