Moda

Teams running AI agents in production face failures that never throw a traditional error, such as fabricated data after a tool call returns empty, order lookups timing out under load, or benign conflicts mistaken for failures. These issues are hard to catch with standard tracing tools because they involve frustrated users, lazy answers, or hallucinations rather than crashes. Without a way to detect and group these mistakes, the same failure can repeat and affect thousands of users before anyone notices. Teams are left reviewing traces manually or discovering problems only after users complain.

Moda reads every production trace end to end and detects failures like fabricated data, timeouts, and hallucinations, letting teams add custom checks in plain language and get alerts in Slack, Linear, or email. Failures are grouped into 'problems' ranked by how many users they affect, with each problem showing the linked traces, the turn where it went wrong, and related problems sharing the same cause. Teams can replay a prompt, model, or tool change against the exact traces that previously failed and compare pass rate, latency, and token usage before deciding whether to ship. Custom signals let teams define specific judges, such as catching incorrect time zone attribution, with hit rate tracking and monitors that trigger alerts under configurable conditions like mention rules, example traces, and auto-muting repeat alerts.