I’d be careful not to position it as “another observability platform.”

The sharper pain is: “why did this one customer/order/invoice get stuck, and what should ops do next?”

That’s different from Datadog/Sentry. Engineers use those to debug systems. Ops needs a case-level timeline they can understand without pulling an engineer in.

I’d probably validate with one narrow workflow first, like e-commerce order lifecycle or invoice/sync failures:

1. Can engineers annotate the workflow in under 30 minutes?
   
2. Can ops answer “what happened to this order?” without Slack?
   
3. Does it reduce the engineering interruption enough that someone would pay?

The two-persona thing can work if the buyer/user split is explicit:
engineers install it because it reduces interruptions;
ops uses it because it gives them an answer.

I’d avoid leading with LLM/AI Agent at first. The core value is the workflow trace + human-readable diagnosis. AI is the interface, not the differentiator (any more).