ClientCoded

QA infrastructure for AI agents. Find where they break.

4 followers

QA infrastructure for AI agents. Find where they break.

4 followers

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AI Coding Agents

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Testing and QA software

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Observability tools

ClientCoded is the QA layer for teams building AI agents. We test conversational and data agents against adversarial scenarios with deterministic ground truth, so the correct answer is known before your agent runs. No LLM-as-judge. 35 test environments with realistic data. Adversarial synthetic customers that try to break your agent. Production monitoring flags failures in real time. Regression testing to catch drift. Fix suggestions and SDK tracing for full observability. Starts at $49/mo.

This is the 2nd launch from ClientCoded. View more

ClientCoded

Launching today

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.

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Launch tags:SaaS•Developer Tools•Artificial Intelligence

Launch Team / Built With

Maker

📌

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?

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Previous ClientCoded Launches

ClientCodedStress-test your AI sales agent before your prospects do

Launched on May 27th, 2026