ClientCoded – QA Platform for AI Agents

Hacker News

Hi HN. We built a QA platform for AI agents. My cofounder spent 6 years at Veeva Systems building QA frameworks for regulated software. I spent 5 years in sales helping AI-native startups with their growth. We've both seen the issues companies face when deploying agents without a structured way of testing and monitoring them. The engineering teams we interviewed prior to building said they are doing manual spot-checks or just using LLM as a judge to check discrepancies. We generate synthetic test environments for 35 platforms (Salesforce, Jira, Stripe, Zendesk, Datadog, and 29 more). Each environment has ~200 adversarial queries with computed ground truth across 7 categories: clean lookups, ambiguous questions, multi-step operations, scope boundary tests, contradictory inputs, invalid assumptions, and context-dependent questions. The ground truth is initially computed by running SQL against the synthetic dataset so it's not just guessed by an LLM. We also do adversarial testing for pre-production conversational agents. The system generates different personas that push agents off-script and scores pass/fail across 10 dimensions so you can see what happens in realistic customer scenarios. Our production monitoring is just one webhook with every conversation evaluated in real time as well. We are two founders bootstrapping and we would love feedback on our approach! https://clientcoded.com/

Visit product View on Hacker NewsSep 13, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, stripe · Missing: mac, macos, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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