◉GEMINI LABJP
●MODEL — Gemini 3.5 Flash reaches GA and now powers gemini-flash-latest●AGENT — Managed Agents enter public preview in the Gemini API, running autonomous, stateful agents in isolated Google-hosted Linux sandboxes●ANTIGRAVITY — The Antigravity Agent managed agent hits public preview, planning, coding, managing files, and browsing the web in its sandbox●DEPRECATION — Image generation models are scheduled to shut down on August 17, 2026; plan your migration●TTS — gemini-3.1-flash-tts-preview now streams speech generation via streamGenerateContent for lower latency●ENTERPRISE — Gemini 3.5 Flash is generally available across the Global, US, and EU regions on Gemini Enterprise●MODEL — Gemini 3.5 Flash reaches GA and now powers gemini-flash-latest●AGENT — Managed Agents enter public preview in the Gemini API, running autonomous, stateful agents in isolated Google-hosted Linux sandboxes●ANTIGRAVITY — The Antigravity Agent managed agent hits public preview, planning, coding, managing files, and browsing the web in its sandbox●DEPRECATION — Image generation models are scheduled to shut down on August 17, 2026; plan your migration●TTS — gemini-3.1-flash-tts-preview now streams speech generation via streamGenerateContent for lower latency●ENTERPRISE — Gemini 3.5 Flash is generally available across the Global, US, and EU regions on Gemini Enterprise
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◈ Gemini API/2026-07-06Advanced

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.