Why tracing alone doesn't catch AI agent failures
Every AI agent team has tracing. LangSmith, Datadog, Arize, Phoenix. They see every tool call, every API response, every latency number.
The trace looks clean. The output is wrong.
The agent called get_invoice. It returned data. The response was fluent. The number was incorrect because the agent called the wrong customer ID. Nothing in the trace flags this, because the trace only shows what happened, not whether the outcome was right.
That is the gap between tracing and testing.
Tracing: "The agent called get_invoice(4821) and it returned $299."
Behavioral testing: "The agent gave the wrong billing amount to the customer at turn 4."
Root cause, both combined: "The agent called get_invoice with customer_id 4821 instead of 1482. The invoice returned belonged to a different customer. The response was fluent and confident and wrong."
We built a platform that connects both sides. Behavioral scoring tells you what broke from the user's perspective. SDK tracing tells you why internally. Fix suggestions tell you how to address it. Change detection verifies the fix worked.
Trace → Diagnose → Fix → Verify.
We have not found another tool that closes this loop.