Launched this week
QAgent
Automated QA for AI agents. Stop shipping on vibes.
50 followers
Automated QA for AI agents. Stop shipping on vibes.
50 followers
Automate AI agent quality testing. Score correctness, detect hallucinations against ground truth, verify policy adherence, and benchmark RAG before bad responses reach real customers.
QAgent
QAgent
Hey Product Hunt! 👋
I am Abhiram, the creator of QAgent.
I built QAgent after watching my own AI agents pass every single playground test, only to break in front of real customers.
The harsh reality of building LLM agents is that single-turn benchmark scores do not translate to multi-turn reliability. If your agent is 95% accurate per turn, the compounding failure rate means it has barely a 35% chance of finishing a 20-turn conversation without hallucinating or breaking rules.
Most solo developers cannot afford to write 200 lines of brittle Python evaluation scripts every time they tweak a system prompt.
QAgent is a zero-bloat automated QA and regression testing platform. You connect your agent through a simple webhook in two minutes without installing any SDK. You can run automated test suites across factual groundedness, policy compliance, RAG faithfulness, and multi-turn context retention, and get a clear 0-100 quality scorecard showing the exact turn where your bot drifted.
There is a free tier with 100 evals every month with no credit card required so any builder can test their bots today.
I would love to hear your feedback, questions, or feature requests!
Dial
"Stop shipping on vibes" is uncomfortably accurate for where most of us are with agent QA right now - regression testing against ground truth instead of eyeballing transcripts is exactly the gap. Scoring policy adherence specifically is the part I'd actually pay for, since that's the failure mode that's hardest to catch by just reading a few sample outputs.
QAgent
@galdayan Appreciate this so much Gal. You hit on the exact reason policy adherence was the hardest metric to dial in.
On the surface, an agent's answer can sound totally polite and reasonable, but underneath it quietly promised an unauthorized discount or return window. Early on, I was constantly fighting false positives where the evaluator would fail an agent just for phrasing things differently, or false negatives where a broken rule slipped through because the bot sounded confident.
I would genuinely love to see how it performs on your agent's policies and get your feedback. There is a free tier at qagent.in that takes two minutes to hook up via webhook, or feel free to message me directly if you want me to help you set up a policy test suite for your setup!