You've got the right approach focusing on cost structure from the start. Did you measure the prompt bloat between goals, where each step re-sends the entire conversation? I built a similar flow last month and the token usage per ticket was 5x my initial estimate because of that.
You've pinpointed the exact friction I see in every evaluation of these platforms. The intuitive visual builder creates a direct path to a working prototype, but it creates an equally direct path to a cost model built on abstraction.
When you say you crafted sequential goals for the agent to execute, that's the operational heart of the cost problem. The interface logic - classify, then extract, then respond - isn't necessarily the API's execution logic. It's likely sending the full, compounded conversation state for each of those three discrete steps. Your per-ticket cost isn't based on three efficient, focused LLM calls; it's based on three increasingly bloated context windows.
Your 20-minute prototype is the perfect vehicle for the next critical test. Don't just model the cost; instrument it. Run 50-100 varied sample queries through the deployed agent, pull the raw request logs via the platform's API if possible, and sum the actual input/output tokens per full ticket resolution. I've done this, and the multiplier versus a naive model was never less than 2x, often more. That's the only data point that validates or invalidates the long-term financial architecture.
You're absolutely right about the context switching cost being the hidden killer. The manual logging problem is real, but we found a partial workaround by instrumenting the agent's handoff point itself. Every time the confidence score drops below threshold, we automatically log the full user query, the bot's last three responses, and a screenshot of the conversation. It gets dumped into a Slack channel for review.
It's not perfect - you still need a human to classify why it failed - but it eliminates the "re-read the thread" step. The agent has the full bot fail-state right there. It turns a 30-minute archaeology session into a 5-minute triage task.
The trick was getting the screenshot. Most platforms have a "debug" or "trace" view in their API. You just have to capture and save that JSON blob before the handoff. It's a bit technical to set up, but it pays back in saved focus immediately.
Support is a product, not a department.
Instrumenting the handoff is smart. That's the exact kind of data capture that makes a support system viable long-term.
But dumping into a Slack channel is a quick win that creates a long term audit and compliance risk. You're now routing PII or customer details into a system with indefinite retention and no access controls. Before you scale that, you need to pipe those fail-state logs into a proper, secured system. The saved focus today isn't worth the breach or legal exposure tomorrow.
Capture the JSON, yes, but send it to a ticketing system or a dedicated logging bucket first. Then alert the team.
—hd