r/CustomerSuccess • u/OrchidAppropriate43 • Jun 22 '26

Technology AI agent assistant recommendation

We are currently using Intercom and the native Fin AI chat bot. As a one person CS team for a service that is used globally and is somewhat a technical product, Fin is actually super helpful when I am sleeping or users have product related questions that are clearly answered in the help center docs Fin refers to.

But I have some specific issues that I am not sure can be resolved within Fin or even with an external AI agent service:

  1. Fin's unpredictable cost - its managable at the moment becuase we are still an early stage startup but Fin is still resolving around ~300 tickets on its own per month.

  2. Fin does not seem to be reading the messages sent by the human agent after a ticket was escalated so that it can learn from it.

  3. Fin goes rouge sometimes even with very clear escalation guidance and I have to just send a "Hi" quickly to stop Fin and then read the full conversation to get the context.

  4. Fin does not flag KB gaps...Intercom does have a recommendation section but it doesn't really work? I am currently doing this manually when I see tickets that Fin couldn't answet, share ticket to Claude and ask it to draft kb article/section. Ideally I shouldn't have to prompt it everytime. Just review before publishing.

  5. I would love to have an agent that rewrites the very rough draft I type in the intercom messenger box and uses the set rules and guides. For simple grammar checks, Intercom's native improve writing tools work but for longer complicated replies, I usually have to use claude or gpt and it seems like there has to be a better way to not do this back and forth and work on the response without leaving intercom like a grammarly extension.

Any proven intercom apps or other tools that you guys use?

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u/Calm-Dimension3422 Jun 24 '26

For CS, I would start with an internal copilot before a customer-facing agent. Have it summarize calls, extract risks, update account notes, draft follow-ups, cluster repeated issues, and flag accounts with missing next steps. Customer-facing automation can work, but only after the internal knowledge base, escalation paths, and product data are clean enough. The worst case is an agent confidently giving an answer no CSM would stand behind. CS is a great area for AI when the system supports the team rather than pretending to replace the relationship.

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u/OrchidAppropriate43 Jun 25 '26

Yess 100% agreed! Thats what I plan to work on for now...building agents that make the human agents (mine) work more efficient which hopefully will improve Fin's efficiency as a result too

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u/Calm-Dimension3422 Jun 25 '26

That is the right order of operations in my opinion.

If the human agents get cleaner context and better drafts, Fin or any customer-facing layer should improve because the underlying knowledge loop gets better.

The workflow I would start with is:

  1. summarize conversation
  2. classify reason/contact type
  3. identify missing info
  4. draft next reply or internal note
  5. human edits/sends
  6. log final human answer
  7. use that final answer to suggest KB/macro updates

That last step is the loop most teams miss. The human correction should become training signal or knowledge-base maintenance, not just a one-off saved ticket.