An assistant gives an incomplete answer. A report stops halfway through. A team says the bot is down. These can look like the same problem from the user's side, but they can require very different engineering work.
The first useful question is concrete: where did the task stop making progress?
In the LEED Agency deployment, deeper reporting depended on client-specific rules, previous reports and files. The engineering work included organizing that knowledge, adding retrieval and adjusting how longer tasks ran. A reporting assistant needs a reliable route to the right context before a better instruction can help.
ConvertSail showed a different failure mode. A tool could be connected through one route while the assistant checked another and reported that it was unavailable. The correction was to define which integration was authoritative and give the system a predictable way to use it. Asking the user to reconnect an already working service would have added another step without fixing the cause.
A separate investigation into a media-production workflow made the distinction especially clear. A complaint that the bot was unavailable included a channel-access problem. Provider reliability and output quality were separate issues. The documented work separated them, checked the service and task records, and produced controlled samples for further review. A running process, an accessible channel and an acceptable result are three different things to prove.
These examples suggest a practical way to review an AI workflow. Start with the user's request and identify the expected output. Check the source the system should read, the route it should use, the permission it needs and the record that should exist afterward. Then inspect the exact point where reality diverges from that sequence.
A dashboard can help when it exposes those states clearly. “Working” is less useful than showing whether the system is retrieving context, waiting for access, preparing a draft or asking for a decision. The interface should make the next step understandable to the person responsible for it.
This is the engineering work around the model: giving an otherwise capable system the right context, access and operating path. The goal is a workflow the team can use and inspect, with failures specific enough to fix.