Your AI agents break. We find out why and fix it.
We watch every agent run, find the failures that keep happening, and turn each one into a fix you can review. A code patch, a guardrail, better context. You decide what ships.
Why DeepProbe
You know your agent failed. But you have no way to learn from it and make the next run better.
We find why they break and turn the repeating patterns into fixes you control.
Failures stay invisible
Your agent books the wrong flight. You find out from a customer complaint three days later. By then it has happened 40 more times.
Same bugs, new disguises
The same failure keeps showing up in different runs. Wrong constraints, missed steps, bad tool calls. Each one looks unique until you group them.
Manual review doesn't scale
Reading traces works at 10 runs a day. At 10,000 it is impossible. You need something that watches every run automatically.
Auto-fixes are risky
When your agent handles real customers and real money, you cannot let a black box quietly retune it. Engineers need to approve every change.
How it works
From failure pattern to shipped fix. You control the whole thing.
Ingest
Connect your traces
SDK, API, or log export. Any framework, any model.
Cluster
Group by root cause
Not just "it broke." Exactly what failed, how often, and why.
Propose
One fix per cluster
A code patch or a runtime guardrail. Annotated so you can review it.
Ship
You approve, we measure
Nothing goes live without your sign-off. We track if the fix actually worked.