Sam Khan

We hear both extremes. Some teams want AI that only drafts, and a human clicks send on everything. Others want full autonomy.
Where we've landed: let the AI act freely on routine work inside clear rules (book the meeting, update the CRM, send the reminder).
Anything outside the rules, like a non-standard discount or a dispute, waits for a named person. The offer stays unchanged while approval is pending.
It's a middle ground, and we keep adjusting where the line sits for each customer.
Where would you draw it? Which actions would you never let an AI take on its own?
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A demo shows what an AI agent can do. A release should prove what it won't do. Before any of our AI employees goes into production, we test three cases:

1. The customer asks for something unapproved, like a non-standard offer. The agent must route it for review and never apply it.

2. A system write fails, for example the CRM update times out. The agent must not report success. It retries safely or escalates.

3. The knowledge base doesn't support an answer. The agent asks a clarifying question or hands off, instead of improvising.

If any of these fail, the release waits. It's slower, but it's why enterprise teams let the agents act at all.

What's on your pre-launch checklist for AI features? What failure taught you to add it?

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