How Do You Troubleshoot Incorrect AI Answers?

Our agents are built with explainability and reasoning inspection tools

baked in. So you can dig into each question to understand how it was

processed, and you can understand how the LLM was thinking about it.

So you can find how a certain answer was computed.

In the rare cases where we completely missed the mark, you can follow the path of

thoughts and pinpoint the layer that made the mistakes.

Did the agent call the wrong API, for example?

And in that case, it's usually an issue in the semantic layer.

It misunderstood that a certain dimension meant A, but instead it thought it would

mean B. Like for example, there was a missing or an ambiguous description.

Or in other cases, the instructions were unclear in the agent configuration.

Or in some other cases, the prompt of the user was unclear.

Or we can find out from the train of thoughts, the reasoning process, if the

calculation itself came out wrong because the planning model contains the mistake.

And in that case, that would be wrong also in the dashboard.

Then at that point, if the planning model, for example, has an aggregation that is

wrong or has a formula that is wrong, well, even if you have a pre-AI

dashboard or one of the traditional user interfaces, numbers would be wrong there

as well. And so through that explainability and reasoning inspection tools, we can

see, is the number coming out wrong from board?

Is the reasoning of the agent at fault?

So we have all those instrumentation to do the launch.