From the source
Cursor's agents rebuilt Chrome's rendering pipeline.
But try to run these agents on a production workload and they don't fail gracefully.
They fail silently, expensively, and differently every time.
Consider a simple use case of analysing financial reports reported by public listed companies.
To answer questions like “which manufacturing companies are investing in supply chain localization and are their projects ramping up as expected”, you will need to extract structured & unstructured data like management commentary, capex investments from balance sheets and so on.
For a certain universe of questions, we roughly need to extract ~200 fields like financial metrics, business metrics, announcements, related party transactions, shareholding patterns, risk factors & industry details.
This is ETL.
The most well-understood workload in software.
At first glance, this looks like something frontier model agents should be able to handle.
We set on a 24 hour sprint to find out.
In parallel, we used the frontier model to author a multi-agent system using Arya, our agent orchestration stack.
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