I’m working on Komo AI, an AI operations platform, and one design question keeps coming up: When an AI agent researches a topic or completes a multi-step workflow, what should it show while working? A final answer alone is not enough for professional use. A trustworthy system should ideally expose: the sources it used; the assumptions it made; the steps it completed; the uncertainty in the result; the points where human approval is needed. Komo explores this through cited web research, document analysis, structured research tables, and executable playbooks. The technical challenge is not simply getting a model to produce an answer. It is making the process inspectable enough that someone can decide whether the result is reliable. That means preserving source context, separating retrieved information from generated interpretation, and making multi-step execution visible rather than presenting everything as one opaque response. I’m affiliated with Komo, so this is not an independent review. I’m interested in the broader engineering question: what level of transparency would make you comfortable delegating real research or operations work to an AI agent? Project and documentation: https://komo.ai/ submitted by /u/Harshit-24
Originally posted by u/Harshit-24 on r/ArtificialInteligence
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