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.
Should You Move Your Agent Loop to Gemini's Managed Agents? Three Questions That Decide What Migrates
With Gemini API's Managed Agents in public preview, deciding between a self-hosted agent loop and a Google-hosted sandbox is now a real question. Three questions — execution environment, state ownership, and failure recovery — decide what migrates and what stays.
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.