Your agents already produce the most valuable data there is: real workflows, and where they went wrong. Mati turns it into clean, labelled data, so your agents learn and improve.
See the problem See how Mati worksEvery day, AI agents run real workflows with real customers, real edge cases and real mistakes. That is exactly what it takes to make an agent better, and nobody can recreate it in a test lab. But it just sits in logs.
Thousands of long, tangled traces full of retries, duplicates and tool noise. The few steps that matter are almost impossible to find.
Teams can’t easily tell which failures keep repeating, why they happen, or what the right behaviour would have been.
Without clean, structured examples, the lessons never flow back into prompts, tests or training. So the same failures keep happening.
The result: agents repeat the same mistakes, and the best data a company has for improving them goes to waste.
Mati takes the logs from your agents, whatever tool they came from, and refines them into a clean dataset. Each failure is found and labelled, with the root cause and the correct fix. Private details are masked along the way. Press run to watch a sample go through.
A structured dataset of real workflow failures, each with what went wrong, why, and the correct fix, plus a report showing which problems matter most. Use it to improve prompts, write sharper tests and train better agents.
| Trace | Failure type | Root cause | Fix |
|---|---|---|---|
| tr_4411 | Step repetition | Retried verification without reading its result | Read result before retry |
| tr_4416 | Ignores the brief | Dropped the date range from the task | Pass constraints to the tool call |
| tr_4420 | Skips checks | Answered before confirming the record | Add a confirm step |
Sample outline
Illustrative sample.