You spent months fine-tuning that AI agent. It’s finally hitting 95% accuracy on your test set. You’re proud. Then the bill arrives—and you realise you’ve built a machine that’s too expensive to run. The panic sets in. You’re not alone.
Every week, I talk to teams who’ve shipped agents that perform beautifully in the lab and bankrupt them in production. The problem isn’t capability. It’s cost predictability. Most developers treat cost like an afterthought. That’s a billion-dollar mistake.
You’ve probably noticed the pattern: each accuracy gain comes with a hidden price tag. A 2% improvement in recall might triple your inference costs. Without a way to measure both sides, you’re flying blind. The industry is obsessed with leaderboards, but the real metric is cost per correct action.
That’s why Jonathan Langens built Maverik. He calls it ‘JMeter for agents’—a systematic way to benchmark performance and predict costs before you deploy. The idea is simple: give developers a tool to quantify the trade-off. ‘I built it,’ he says, ‘because I want to be able to assess improvements and predict what an agent would cost in a real business setting.’
I saw the pain firsthand. A team I worked with spent weeks improving accuracy by 2%, only to discover their costs tripled. They had no data on the trade-off. They were guessing. If you can’t measure the cost of a capability gain, you’re not engineering—you’re gambling.
Here’s the twist: the real bottleneck to production AI isn’t intelligence. It’s cost predictability. The smarter your agent gets, the more expensive it becomes. Without a way to model that curve, you’ll never know if a 5% accuracy gain is worth a 10x cost increase. Most teams optimise for the wrong variable.
Maverik changes that. It lets you define test suites, run benchmarks, and see exactly how much each improvement will cost. It’s open source, it’s practical, and it’s the kind of tool that should have existed years ago. The developers who adopt this mindset will ship agents that actually work in the real world. The rest will keep burning money.
The best AI agents aren’t the most accurate ones. They’re the ones you can afford to run.
FAQ
Q: Isn't accuracy the most important metric for an AI agent?
A: Accuracy matters, but only if you can afford to deploy the agent. A 95% accurate agent that costs $10 per call is useless at scale. The real metric is cost per correct action, not raw accuracy.
Q: How do I use Maverik to make better decisions?
A: You define test suites for your agent, run benchmarks, and get a clear cost prediction per task. That lets you compare different models or configurations side-by-side, so you can optimise for the trade-off between performance and expense.
Q: Isn't this just a way to scare developers away from improving agents?
A: No—it's a way to make informed bets. If you know a 5% accuracy gain costs 10x more, you can decide whether that's worth it for your use case. The contrarian truth is that most agents are already good enough; the bottleneck is making them cheap enough to run.