Most companies deploy AI sales and support agents with no way to measure if they're helping or hurting. The data says the gap between good and bad AI experiences is massive, and most businesses are on the wrong side of it.
AI chatbot adoption has exploded. Over 80% of companies are using or planning AI-powered chat for customer interactions. But adoption doesn't mean effectiveness.
This isn't limited to support. Sales agents face the same problem. When an AI SDR can't handle a skeptical prospect, fumbles a pricing question, or tries to qualify someone who's clearly the wrong fit, that visitor leaves and never comes back.
The conclusion is clear: customers don't hate AI. They hate untested AI that doesn't work. And most companies have no way to tell the difference until customers start leaving.
Even if your AI agent works, there's a separate problem: your website is open 24/7 but your sales team works 8 hours a day. What happens to the other 16 hours?
That delay is catastrophic. The data on speed-to-lead is unambiguous:
When a prospect visits your website at 9pm, fills out a form, and waits 42 hours for a response, they've already talked to your competitor. An AI SDR responds in seconds, qualifies them through conversation, and books a meeting on your calendar before they close the tab.
AI agents are the fastest-growing category in enterprise software. The scale of deployment is unprecedented.
Every one of these AI agents is being deployed with the same approach: build it, demo it internally, push it live, and hope it works. Nobody is stress-testing these agents against realistic failure scenarios before they face real customers.
That confidence gap is the opportunity. Companies need to know their AI agent works before it talks to customers. That's what ClientCoded Agent Testing provides.
We built ClientCoded because we sat in the seat these tools are made for. After years as an Account Executive qualifying thousands of startups, managing SDR teams, and training reps across industries, the same patterns kept showing up: agents that couldn't handle pushback, leads that went cold because nobody responded, and tools that promised automation but delivered more work.
ClientCoded is three products on one platform, each solving a specific problem:
Software teams wouldn't ship code without running tests. But every day, companies push AI agent updates to production without any quality assurance. They change a prompt, redeploy, and the agent handles real customers differently with no one watching.
ClientCoded Agent Testing exists to close this gap. We generate synthetic prospects that test every failure mode: hostile objectors, wrong-fit leads, confused users, escalation seekers. We score every conversation across six dimensions. We monitor weekly and alert you when performance changes.
The result: you know your agent works before your customers find out it doesn't.
ClientCoded wasn't built by engineers who read about sales in a textbook. It was built by an Account Executive who spent years on the front lines: managing SDR teams, training reps across industries, qualifying thousands of startups, and watching deals die because tools failed at the moment they mattered most.
Every feature exists because it solved a real problem on a real sales floor. The lead scoring thresholds came from years of qualifying prospects and knowing which signals actually predict a deal. The objection handling came from sitting on calls where reps froze when a prospect pushed back. The agent testing came from deploying an AI SDR and realizing there was no way to know if it was good until a real prospect had a bad experience.
We used Agent Testing on our own AI SDR. It scored a D on the first run. We found eight issues we never would have caught manually. After iterating on the prompt based on the scorecard feedback, it scored a B. That experience validated the product better than any customer interview.
Test your agent for free in 10 minutes. No account required. Or book a call to see how the AI SDR captures the leads you're currently missing.