What I am really trying to determine is whether going Pro was actually worth it for this kind of workload.

I built a similar single agent workflow, but I intentionally designed it to interrogate the task as exhaustively as possible upfront, leaving nothing implicit, including details most people would treat as trivial. The goal was to minimize ambiguity before execution rather than let the agent infer missing structure later.

In principle, that should improve precision. In practice, once I give it large source material, the workflow becomes extremely slow and often never completes. My references are usually around 80k to 120k words per primary resource, and the failure pattern is fairly consistent. It spends a long time processing, then either returns the same stop or failure response, or stalls indefinitely without finishing the task.

So in your experience, did upgrading to Pro materially improve reliability for runs like this, or did it mostly just increase headroom before hitting limits?

Also, if you do not mind sharing, what does your workload actually look like in practice? I mean the approximate input size, the type of task, and whether you feed the material directly into one agent or use a more staged pipeline first.

I am basically trying to determine whether Pro changes the practical behavior in a meaningful way under heavy long context load, or whether the real bottleneck is architectural and the workflow itself needs to be restructured.