AI Research Scientist
An autonomous research system that formulates scientific hypotheses, gathers empirical datasets, executes automated benchmarks, and publishes synthesized findings.
The Problem
What difficult challenge is this experiment tackling?Investigating complex web engineering problems (e.g., the precise relationship between Core Web Vitals and organic conversions) takes weeks of manual literature search and data scraping.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?An agentic research loop can independently formulate falsifiable hypotheses, pull ArXiv/academic literature, scrape real-world performance datasets, run statistical regressions, and compile formal research reports.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionEmpirical validation of findings, citation accuracy, statistical p-value verification, report generation turnaround under 45 minutes.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsStrict rate-limiting on external web scrapers, automated sanitization of proprietary company telemetry.
Lessons Learned & Observations
Empirical findings from prototyping and testingHypothesis generation boundaries established. Prototype awaiting scheduled testing run.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesConnect real-time GA4 anonymous event streams as empirical research inputs.