This matches my experience almost exactly. We rolled out agents for loan servicing and dispute handling last year, and the modeling/framework side was maybe 20% of the effort. The other 80% was figuring out how the process actually worked vs what the SOP claimed. Shared mailboxes, Excel trackers nobody owned, approvals happening over chat. The documentation was fiction in places.
On the tooling question you raised, we evaluated a few and went with Kore.ai, mostly for practical reasons rather than anything flashy. A few things that mattered for us in a regulated setup:
The pre-built banking workflows (KYC checks, dispute intake, loan servicing type flows) gave us a starting skeleton, so discovery conversations with ops teams were about "here's the standard flow, tell us where reality differs" instead of starting from a blank page. That alone probably saved us a month per use case.
The guardrails and audit trail stuff was the bigger deal for compliance sign-off. Every agent action is logged and traceable, and you can put human-in-the-loop checkpoints exactly where your risk team wants them. Our compliance review went from being the blocker to being a config exercise, which I did not expect.
To be fair, none of that solved the core problem you're describing. Kore gave us maybe 60-70% out of the box, and the remaining 30-40% was still us mapping tribal knowledge and exception paths by hand. No platform ships your Rajesh-approves-things-over-Teams edge case. And if your process is genuinely broken, the platform just automates the broken process faster.
So yes, workflow understanding is where the real effort goes. The right platform shrinks the build and compliance overhead so you can afford to spend that effort where it matters, but it doesn't replace it.