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DecGuard
Reliability testing for probabilistic AI decisions.
Reliability testing for probabilistic AI decisions in system-one-models: you describe the decision once in a small Decision Contract, run a dataset through any backend, and get one reproducible reliability report with CI-friendly PASS / WARN / FAIL gates. DecGuard measures accuracy and calibration, fuzzes behavioral invariants, compares model versions, and checks production records offline.
I’ve been building an open-source tool called DecGuard around a problem I kept coming back to: testing probabilistic decision systems is a bit different from evaluating general-purpose LLM output.
If a model is supposed to return something structured like a choice, a boolean decision, or an ordered score with probabilities, there are some fairly concrete things you probably want to know:
does reordering the options change the answer?
does harmless formatting change the distribution?
did calibration get worse after changing the model/backend?
can I reproduce a failure later?
can CI reject a version that violates one of these assumptions?
what happens to reliability on production records or specific segments?
DecGuard puts those things into a small decision contract and uses the same contract for normal tests, metamorphic fuzzing, regression comparisons, replay, production checks, and reliability gates.
It currently supports Choice, Noul/boolean and Score decisions, and the backend layer is deliberately interchangeable. I’ve tested the System One integration end-to-end with real Kev and Jev backends as well.
It’s still an early version, so I’m mostly interested in whether the abstraction itself makes sense to people building this kind of system, and where it breaks down in real use.
About DecGuard on Product Hunt
“Reliability testing for probabilistic AI decisions.”
DecGuard was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #56 on the daily leaderboard. Reliability testing for probabilistic AI decisions in system-one-models: you describe the decision once in a small Decision Contract, run a dataset through any backend, and get one reproducible reliability report with CI-friendly PASS / WARN / FAIL gates. DecGuard measures accuracy and calibration, fuzzes behavioral invariants, compares model versions, and checks production records offline.
On the analytics side, DecGuard competes within Open Source, Artificial Intelligence and GitHub — topics that collectively have 590.2k followers on Product Hunt. The dashboard above tracks how DecGuard performed against the three products that launched closest to it on the same day.
Who hunted DecGuard?
DecGuard was hunted by Lorenzo. 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 DecGuard including community comment highlights and product details, visit the product overview.
Lorenzo