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ClientCoded
QA infrastructure for AI agents. Find where they break.
Most agent testing either traces failures after customers hit them, or makes you hand-write every test and let an LLM guess if it passed. ClientCoded generates adversarial tests and knows the correct answer, scoring against computed ground truth with SQL, not opinion. It catches failures before production, monitors every live conversation with Slack alerts, and turns real production failures into new regression tests automatically. Connect in minutes with one webhook. First audit free.
Hey Product Hunt! Travis here, co-founder of ClientCoded.
We built this because we kept seeing the same problem: teams ship an AI agent, it works great in demos, then it falls apart in production when real users push back, ask unexpected questions, or feed it bad data. By the time someone notices, customers have already had a bad experience.
The deeper issue is how most tools try to solve this. They use an LLM to evaluate another LLM’s output, which means the scoring layer hallucinates just like the agent being tested. You end up with tests that pass when they shouldn’t and fail when they shouldn’t.
We went a different direction. ClientCoded computes ground truth from the test data with SQL before the agent ever runs. The correct answer is deterministic. No LLM-as-judge, no subjective scoring. When your agent gets something wrong, we can tell you exactly what it got wrong and what the answer should have been.
We also run adversarial synthetic customers that lie, contradict themselves, go off-topic, and try to break your agent the way real users do. And if something does slip through to production, our monitoring catches it and lets you save it as a regression test with one click so it never happens again.
As a thank you to the PH community: free production monitoring for anyone who wants it. One webhook, Slack alerts when your agent conversations go sideways, no time limit. Just reply here or DM me and I’ll get you set up.
Would love to hear from anyone building or managing AI agents. What’s the hardest part of knowing whether your agent is actually working?
About ClientCoded on Product Hunt
“QA infrastructure for AI agents. Find where they break.”
ClientCoded was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #376 on the daily leaderboard. Most agent testing either traces failures after customers hit them, or makes you hand-write every test and let an LLM guess if it passed. ClientCoded generates adversarial tests and knows the correct answer, scoring against computed ground truth with SQL, not opinion. It catches failures before production, monitors every live conversation with Slack alerts, and turns real production failures into new regression tests automatically. Connect in minutes with one webhook. First audit free.
On the analytics side, ClientCoded competes within SaaS, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how ClientCoded performed against the three products that launched closest to it on the same day.
Who hunted ClientCoded?
ClientCoded was hunted by Travis Cronin. 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 ClientCoded including community comment highlights and product details, visit the product overview.
Travis Cronin