It's nearly impossible to prevent LLMs from hallucinating, which creates a significant reliability problem. Enterprise companies think, "Using agents could save me money, but if they do the job wrong, the damage outweighs the benefits." However, there's openness to using agents for non-customer-facing parts and non-critical tasks within the company.

The developers of an e-commerce infrastructure approached us because the format of manufacturer's files doesn't match their e-commerce site's Excel format, and they can't solve it with RPA due to minor differences. They asked if we could perform this data transformation reliably. After two weeks of development, we implemented a reliability layer in our open-source repository. The results were remarkable:

Pre-reliability layer: 28.75% accuracy (23/80 successful transfers)

Post-reliability layer: 98.75% accuracy (79/80 successful transfers)

At Upsonic, we use verifier agents and editor agents for this. We didn't expect such high success rates from the agents. I'm surprised by how common these data transformation tasks are. This could be a great vertical agent idea. Btw we use this source (https://arxiv.org/pdf/2501.13946)