Google Cloud launched the Google Cloud Gemini agent, a cloud-hosted delegation layer for enterprise work covering question answering, knowledge work, media creation, and coding. It includes 3 governed cost controls, including real-time spend caps, and can pause an agent when a hard per-project limit is hit in the Cloud Billing Console. This shifts enterprise AI use toward a single agent interface (1 prompt box and 1 API) with persistent cloud execution, governed identities, and routed model execution across Gemini and Claude.
Google Research published a tutorial that implements RRSI (Regularized Recursive Self-Improvement), letting an LLM agent rewrite its own harness (prompts, tools, memory, control flow, sub-agents) while keeping a frozen underlying model, and driving the paper’s edit-selection logic directly with Python. The selection procedure uses calibrated noise bands, with the paper reporting a value of 0.020 for engineering evaluations. As a result, the guide turns the paper’s rules into runnable code, including how scores treat missing trials, and demonstrates how RRSI’s decisions can be audited against ground truth in a simulated setting rather than only by comparing top scores.
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