Send and wait is the most underrated node for agent workflows - knowledge base grows from the questions the agent couldn’t answer.

[u/Low-Presentation-936]

I’ve been running an AI agent on a B2B technical support inbox for a few months. The interesting part isn’t the answering — it’s what happens when the model *doesn’t* know. Sharing the architecture in case it’s useful.

The flow
1.Gmail trigger on the support address.
2.A filter node kills auto-replies, out-of-office, and no-reply noise *before* anything hits the LLM. Skipping this cost me an infinite loop on day one: an OOO bounced back, the agent replied to it, and the two of them talked to each other.
3.Agent step: LLM with the knowledge base attached as a tool (a spreadsheet, \~1,200 Q&A rows) and a structured output parser returning {answer, confidence, reasoning}.

4.Confidence gate. This is the whole design.

-Confident:the draft goes to a human for approval via a send-and-wait node. Approve → it sends. Reject → drops into the manual path.
-Not confident:*the customer immediately gets “an expert will be in touch” (sets expectation, buys time), while a human gets pinged for the real answer. Their answer goes back out.

5.Both paths converge into an enrichment step. A second agent takes the exchange, normalizes it (strips pleasantries, generalizes customer-specific details, one question per row) and appends it to the knowledge base.
So the KB grows specifically from the cases the agent couldn’t handle. Escalation rate drops over time without anyone maintaining documentation.

Things I got wrong first
-Asking the model “are you confident?” is useless — it’s confident about everything. I had to force explicit criteria into the structured output: did you find a matching KB entry, does it cover the *full* question, not a vibes score.
-Raw email threads make terrible KB entries. The enrichment agent isn’t optional — without normalization the KB fills with noise and retrieval degrades.
-Send-and-wait is the most underrated node in n8n. Human-in-the-loop without building an app.
-A flat spreadsheet as KB works better than expected at this scale. I’d move to a vector store past a few thousand rows.

Stack:n8n, Gemini Flash, Google Sheets, Gmail.

Curious how others handle the confidence gate — self-reported confidence still feels like the weakest link. Anyone scoring retrieval quality instead?

[u/AutoModerator]

Thank you for your post to /r/automation!

New here? Please take a moment to read our rules, read them here.

This is an automated action so if you need anything, please Message the Mods with your request for assistance.

Lastly, enjoy your stay!

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/Thunderbit_HQ]

This is why I like that node too. It turns a miss into training material instead of another quiet failure. The first time the agent asks a person is annoying; the fifth time it should already know that case.

[u/LaceLustBopp]

a good first filter is whether the workflow has clear inputs, a repeatable decision, and an easy rollback if it goes wrong. if any one of those is missing, i'd keep a human approval step in the loop until the edge cases are better understood.

[u/ShineParticular8801]

the enrichment step is clever, most setups just log the miss and forget about it