This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Reliability is the next frontier for AI apps. We were already shipping MVPs. It's time to turn them into products. Existing observability tools do for LLM apps what logs did for conventional ones: they tell you what happened, not what changed. Regent is the first regression testing layer for agentic apps. It can run semantic diffs on your agent's entire execution trace for any critical inputs, all before you merge the PR. Posts the results right in your Github. No more finding out from users.
Hey Product Hunt 👋 I'm Agrim, and this one's been a while coming.
It started with Jarvis, my AI secretary. Every time I changed a prompt or tweaked the architecture, which was constantly, I had no idea what I'd quietly broken. So I'd manually test scenario after scenario, every single time, knowing it would only get worse as the app grew. I looked for something that could just tell me "this behaviour changed."
Nothing existed for agentic apps. So I built Regent.
The thing I'm most proud of honestly is the onboarding. I was obsessive about it. If integrating a testing tool feels like a project in itself, nobody does it. So I kept going until it was 2 lines of code. That part took some time, but definitely worth it.
What Regent does is simple in concept but surprisingly hard in practice: it records how your agent actually behaves, including traces, nested calls, everything. And diffs it against that baseline on every PR, posting the results straight into your GitHub comment. Not just text output, the full execution trace, actions, flow, decisions.
If you're building LLM apps and you've felt this pain, I'd genuinely love to hear from you. Honest first impressions, what resonates, what feels off, all welcome.
Super interesting problem statement. Love to see how the product evolves to solve it.
Yeah feels like there’s a gap here tbh. Right now we’re mostly just manually testing / eyeballing outputs, which isn’t ideal. Let’s try plugging this into our workflow and see how it goes.
I tried it yesterday and it works great! Really like the product. I would probably start using this in my main workflow now.
About Regent on Product Hunt
“Know when your AI changes behavior”
Regent was submitted on Product Hunt and earned 12 upvotes and 7 comments, placing #13 on the daily leaderboard. Reliability is the next frontier for AI apps. We were already shipping MVPs. It's time to turn them into products. Existing observability tools do for LLM apps what logs did for conventional ones: they tell you what happened, not what changed. Regent is the first regression testing layer for agentic apps. It can run semantic diffs on your agent's entire execution trace for any critical inputs, all before you merge the PR. Posts the results right in your Github. No more finding out from users.
Regent was featured in SaaS (41.7k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.3k followers) on Product Hunt. Together, these topics include over 198.7k products, making this a competitive space to launch in.
Who hunted Regent?
Regent was hunted by Agrim Chopra. 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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Agrim Chopra