# Provenance Gate (solo team)

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Sergey Aganezov](https://cerebralvalley.ai/u/aganezov)
- **GitHub:** https://github.com/aganezov/provenance-gate
- **Demo video:** https://www.youtube.com/watch?v=6qXYLwQWb0s
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/256

I investigated a failure mode of agentic science: as an agent forks approaches, revises an upstream step, and re-runs, a final result can quietly come to rest on stale or version-conflicting inputs. The conflict lives in the provenance, not the prose — so reading the output, a human or an LLM reviewer may have nothing to go on.

To study it in Claude Science I built two instruments: a deterministic, read-only provenance gate that computes two structural verdicts per computation (a stale input; a lineage that reconverges on two live versions of one artifact), and a first-prototype harness that runs a scenario unattended and captures each rollout for grading.

The finding: across 24 unattended rollouts of one PBMC single-cell scenario, 12 shipped a version-mixed figure and 6 shipped it with no warning anywhere in the conversation. Installing the gate as a skill wasn't enough for an agent to use it on its own; one line of standing policy was, and the agent then caught the conflict and stopped to ask. Yet handed the gate's flag directly, an agent still rationalized a real conflict as "benign" — which is exactly why the check must be deterministic: the verdict is read from the lineage, not the model's judgment.

Why it matters: fast agentic science is only trustworthy if results trace to consistent inputs — a structural fact a deterministic check can hold even when the prose can't, and a natural candidate for a verifiable training signal, not just a review. Design rationale, limitations, and full rollout results are in the repo.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/256. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt