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Argmin AI
Test AI agent workflows without ML expertise
For teams and founders shipping agentic AI workflows and features. Generic metrics don't speak your product's language, so a new prompt, model, or RAG change can pass them and still ship real issues. A quick manual check catches even less. You don't need annotated data, an ML team, or a month to be safe from regression. Drop in your agent's task, business rules, docs, and examples, and Argmin AI builds an evaluation you run before every change.
Hi Product Hunt 👋 I'm Dmitriy, co-founder of Argmin AI.
We did not start from a problem we hit ourselves. We started as a company
trying to help product teams avoid major financial losses when their AI and
agentic workflows reach production and meet real usage.
The more time we spent with teams, the more we saw the problem starts earlier.
Most teams have no transparent evaluation metrics. So quality gets checked by
vibe checks, and without business-specific metrics, nobody really knows how
their AI feature behaves.
Agentic AI is not a normal product feature. You cannot cover it with manual
QA, standard automated tests, or basic analytics. Its behavior is dynamic,
context-dependent, and hard to predict. A small change in one line of a prompt
can shift the whole agent, including places you would never expect. So teams
become afraid to change anything.
Standard metrics help when they exist, but they do not describe how the system
behaves in your business logic. They do not speak your language. You can have
high accuracy and still miss the scenarios that matter most to your product.
Then there is data. Most teams do not have a dataset to test on yet, and
building a golden one is a serious project: find the edge cases, label the
data, run the annotation, and get everyone to agree on what "good" means. That
is heavy work for founders, product teams, and ML engineers, who also spend
weeks translating business expectations into evaluation logic.
That is why we built Argmin AI. It creates business-specific evaluation
metrics for your agentic AI, so you can ship changes with confidence. No deep
technical knowledge, no large ML team. If you want to launch or improve an
agentic AI feature, building a reliable evaluation should not be the hardest
part.
I'd love your honest take: how do you check today whether an agent change is
safe to ship?
About Argmin AI on Product Hunt
“Test AI agent workflows without ML expertise”
Argmin AI was submitted on Product Hunt and earned 37 upvotes and 23 comments, placing #26 on the daily leaderboard. For teams and founders shipping agentic AI workflows and features. Generic metrics don't speak your product's language, so a new prompt, model, or RAG change can pass them and still ship real issues. A quick manual check catches even less. You don't need annotated data, an ML team, or a month to be safe from regression. Drop in your agent's task, business rules, docs, and examples, and Argmin AI builds an evaluation you run before every change.
On the analytics side, Argmin AI competes within Developer Tools, Artificial Intelligence and Maker Tools — topics that collectively have 993.4k followers on Product Hunt. The dashboard above tracks how Argmin AI performed against the three products that launched closest to it on the same day.
Who hunted Argmin AI?
Argmin AI was hunted by Dmitrii Konyrev. 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 Argmin AI including community comment highlights and product details, visit the product overview.
Dmitrii Konyrev