Over the past few days I significantly improved ReefWatch AI's autonomous self-improvement system.
What's new Added nightly Cloud Scheduler execution for fully autonomous evaluations Added cost-aware safeguards that skip evaluations when the system is already healthy Implemented historical score tracking and quality trend visualization Added persistent evaluation history across deployments using Google Cloud Storage Fixed score comparison logic to accurately measure improvement vs degradation Added transparent audit logs showing every autonomous check, including skipped runs Improved researcher profile persistence so monitored reefs and alert preferences survive Cloud Run revisions
Result ReefWatch AI now continuously evaluates itself, tracks quality over time, preserves historical performance data, and autonomously decides when intervention is needed, bringing it much closer to a truly self-improving conservation agent.
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