Helix
Built a self-correcting heart disease classifier — an autonomous ML agent that runs the entire training loop itself with no human in it. Given a prediction task on the UCI Heart Disease dataset (Cleveland, Hungary, Switzerland, Long Beach VA, ~920 records combined), the agent collects data, trains, evaluates itself, and corrects course until it beats the published benchmark (83.3% baseline, up to 86.9% best-published), with no human choosing the model, tuning hyperparameters, or deciding how to fix a bad result. It starts deliberately weak with only the smallest hospital source and has to diagnose on its own whether it's underperforming because it's data-starved, the data shape is wrong, or it picked the wrong model class — then pulls in another hospital source live, transforms data through Nexla, discovers and pays for an enrichment service through Zero.xyz mid-loop, or switches models, and retries. Every autonomous action — pulling a new data source, spending on enrichment, swapping models mid-loop — is gated through Pomerium with a spend ceiling, action allowlist, and full audit log, making it possible to demo live and prove after the fact that the autonomy stayed inside real guardrails.