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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?
You don't know what you don't know. If you're building agentic AI workflows and don't have a robust eval system, you're flying blind. This is a really cool solution.
would love to see a github action or CI hook that runs the eval automatically on every PR and comments the diff in the checks section, that would make it actually part of the loop without anyone having to remember to trigger it
Seems to be great tool for AI agentic at scale. Looking forward to give it a try! Is there relevant documentation or guide on how to start using it?
what caught my attention is the focus on evaluation data rather than the evaluator itself. in many ML projects, preparing a reliable evaluation set is where a considerable amount of time disappears. if this can simplify that step, it would be genuinely useful.
Skipping the labeled-dataset step is the smart call here!!
That’s usually the point where teams give up on evals and just go back to vibe checks.
i’m curious how this holds up as an agent’s scope grows, more tools, longer chains. Does the eval keep pace automatically, or do you need to go back and refresh it by hand at that point?
love the direction. most teams don’t have perfectly labeled datasets, and building them from scratch usually turns into a project of its own. starting from production traces instead feels much closer to how things actually work. gonna give it a try with one of our internal workflows and see how it does.
the fact that it generates evals from just a task description and a few examples is genuinely impressive. most eval tools assume you already have a labeled dataset sitting around, so skipping that whole step is a real unlock for smaller teams shipping fast.
the way it builds evals from your own task description and docs instead of forcing you to hand-label data is a really thoughtful move for shipping agent features fast.
Would love a way to compare eval runs side by side so we can see exactly where a new prompt version regressed before merging. A diff view across runs would save a lot of guesswork.
love how you cut straight to the eval problem instead of padding with generic AI agent dashboards. the "drop in your docs and get evals" angle feels like exactly what shipping teams have been cobbling together with brittle scripts for too long.
About Argmin AI on Product Hunt
“Test AI agent workflows without ML expertise”
Argmin AI was submitted on Product Hunt and earned 38 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.
Argmin AI was featured in Developer Tools (516.3k followers), Artificial Intelligence (474.3k followers) and Maker Tools (2.8k followers) on Product Hunt. Together, these topics include over 188.4k products, making this a competitive space to launch in.
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.
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Dmitrii Konyrev