Teams shipping AI agents to production, quietly kept honest.
The runs that look like they worked but didn't are the ones that cost customers. Here's how teams use Glassray to catch them before their users do.
Customer storySuper44 replaced the agent monitoring it built in-house with Glassray and increased agent reliability 2.2x
Super44's agent briefs local merchants on their own sales, staffing, and reviews every morning, so a wrong number costs trust. Glassray replaced a noisy daily spot-check with stateful patterns tracked over time.
2.2xagent reliability736deviation investigations55custom failure patternsRead case study
Customer storyArchitect caught 15x more agent failures and cut investigation time by 19 hours a month
Architect's AI sales agent answers shoppers in real time, so failures are invisible to the brands paying for it. Glassray reads every production trace and surfaces the runs that quietly went wrong.
15xmore failures detected~19hinvestigation replaced / mo77failure patterns surfacedRead case study
Find the failures your evals miss
Glassray monitors your agent's production traces and surfaces the runs where it looked like it worked but didn't.
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