Explore a comprehensive coding guide to Google Research's RRSI (Regularized Recursive Self-Improvement), detailing how noise bands, cost rules, and leakage screens enable safe, efficient, and self-improving AI agents. The post Google Research RRSI Guide: Mastering Self-Improving AI Agents appeared first on MarkTechPost.
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How OpenSmartRoute helps
The router treats agents, tools and skills as targets in the same catalogue as models. An MCP tool or an A2A agent is one entry; the router picks it when it fits the request and learns from how it performs. The same guard and resource limits apply to a tool's arguments as to a prompt.
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.
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