Disclosure: I work on Komo, an AI revenue agent. The hardest part of building agents for revenue work has not been generating text. It has been deciding where the agent should stop. Once an agent can access CRM records, inboxes, calendars, call notes, and web research, a plausible answer is not enough. A wrong action can create a real customer problem. We built around three constraints: Research and tool use run in an isolated cloud environment. Account briefs should show the evidence behind important claims, not just a polished summary. The agent can prepare work—research, drafts, proposed CRM updates—but meaningful actions go into an approval queue. We also log the prompt, tools used, and output for each action. That does not eliminate model errors. It makes them easier to inspect. The trade-off is obvious: approval adds friction, and evidence gathering can be slower than letting an agent act freely. But for workflows touching customer data and external communication, we think “fastest possible automation” is the wrong default. For people building or deploying agents: where would you draw the boundary between an agent preparing work and an agent taking action? submitted by /u/Harshit-24
Originally posted by u/Harshit-24 on r/ArtificialInteligence