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Marginal
Governance that AI coding agents have to earn
Marginal is an open-source governance layer for AI coding agents. It starts in Shadow Mode, collects verifiable runtime evidence, and only earns limited enforcement authority when the evidence supports it.
Hey Product Hunt 👋
I built Marginal around a question I kept coming back to:
When should software actually be trusted to stop an AI coding agent?
Agents sometimes repeat actions without making progress. The obvious solution is to add guardrails and block repetition, but that creates another problem: repetition can be intentional, useful, or necessary.
So Marginal takes a different approach. It starts in Shadow Mode and has no enforcement authority. It observes actions, outcomes, workspace state and evidence. Only after collecting and verifying enough local evidence can it earn narrowly scoped authority to intervene.
Some of the ideas behind it:
→ Shadow Mode before enforcement
→ Earned Enforcement instead of static blocking
→ Hash-chained Decision Ledger
→ Authority can be automatically removed when evidence degrades
→ Local-first and provider-neutral
→ Zero mandatory runtime dependencies
Current integrations include Codex, Claude Code, OpenCode and PrivacyCode. Codex currently supports limited native tool enforcement; the others are observation-only.
I'm also deliberately trying to avoid inflated AI benchmark claims. In one exploratory paired Codex experiment we observed 24.93% fewer effective tokens, but neither lane solved the tasks and Marginal issued zero denials — so we explicitly do not claim Marginal caused that reduction.
It's early, open source, and I'm looking for people interested in coding agents, evaluation, observability and AI governance.
I'd particularly love feedback on one question:
What evidence would you require before trusting a system to block an AI agent action?
About Marginal on Product Hunt
“Governance that AI coding agents have to earn”
Marginal was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #153 on the daily leaderboard. Marginal is an open-source governance layer for AI coding agents. It starts in Shadow Mode, collects verifiable runtime evidence, and only earns limited enforcement authority when the evidence supports it.
On the analytics side, Marginal competes within Open Source, Developer Tools and GitHub — topics that collectively have 628.9k followers on Product Hunt. The dashboard above tracks how Marginal performed against the three products that launched closest to it on the same day.
Who hunted Marginal?
Marginal was hunted by SignalLayer Labs. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Marginal including community comment highlights and product details, visit the product overview.
SignalLayer Labs