Follow a task from request to result.
A business already has software, records and ways of making decisions. An AI feature enters that environment. It may need to find information, interpret a request, call another system and check what happened.
Each connection can change the result. Missing context, stale records or an unreliable tool can undermine an otherwise capable model.
Build the parts the task needs.
Use established products where they fit. Engineer the missing connections and controls. Decide which steps need a model and which can be handled by ordinary software.
Research helps resolve a specific uncertainty: which approach to use, what assumption failed or whether the idea is worth pursuing. The finding should change an engineering decision.
Test the result in its environment.
Evaluate the complete workflow, including permissions, failure and operating cost. In our work with MeshMesh, evaluation checked the resulting Salesforce state after an agent performed a task. The same principle applies wherever AI is authorised to act: inspect what changed in the application.
The operating knowledge study explores one small part of this: how the answer changes when a source is current, conflicting or restricted.