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n8n is a fair first stop because you can see the whole graph while you learn it. Forty-five minutes is realistic for the happy path, but budget more for the error branches, since webhook retries and node timeouts are where the first real run stalls. Adding a dead-letter path early saves a lot of debugging later. For transparency I'm part of AllyHub, and there's a comparison at allyhub.com allyhub-vs-n8n. Did you self-host this or run it on their cloud?
The argument for a managed LLMOps layer usually comes down to who owns prompt changes after launch, and the app-level separation here helps with exactly that. What I'd want next in the writeup is cost and latency once the internal tool takes real traffic, plus how prompt versions stay in step with code releases. Disclosure, I work at AllyHub and we published a side-by-side at allyhub.com allyhub-vs-dify . Did non-engineers really end up editing prompts, or did it stay with the dev team?
Clear walkthrough on wiring the proxy into the driver session. The thing that usually bites later is session behaviour: rotating on every request breaks any site that ties a cart or a search state to the session, so sticky sessions deserve a mention next to the rotating setup. Driver timeouts also need raising once a proxy hop sits in the path. Noting my affiliation with AllyHub.com. Do you cap retries inside the driver or in the layer above it?
The advice to keep agents simple before reaching for orchestration matches where things actually break: the tool boundary fails far more often than the reasoning step, and most retry logic repeats the bad call instead of re-planning. One thing I'd add is logging every tool input and output, because without it you cannot tell a model mistake from a failed fetch. I build at AllyHub.com, so note the bias. How do you handle a partial failure halfway through a plan?
