Cross-model handoff

A cross-model handoff is passing the same piece of work between different AI assistants so each one handles the part it does well. You finish a stage with one tool, then carry the output into another to do the next stage.

A cross-model handoff is passing the same piece of work between different AI assistants so each one handles the part it does well. Rather than expecting one tool to win at every stage, you move the artifact along: one assistant drafts, another researches, another polishes. Say you are writing a launch email for a to-do app. You draft the message with one assistant because its writing reads naturally, paste that draft into a second assistant to gather supporting facts and check claims, then hand the result to a third that is stronger at clean formatting and layout. The email is one artifact that passed through three sets of strengths instead of settling for whatever a single tool happened to be good at.

Builder example

Each assistant has a different profile, and those profiles shift as models update, so locking one tool into every step leaves quality on the table. If your support bot's reply templates need warm wording, careful fact-checking, and tidy structure, you can ask one model for the draft, ask a stronger researcher to verify the details, and ask a layout-focused model to format the final version. The practical stake is that you direct each stage to the assistant suited for it, then keep the same artifact moving instead of restarting from scratch in one tool.

Common confusion: A cross-model handoff moves one artifact through several assistants in sequence so each contributes a stage. Running the same prompt in two assistants to compare their answers is a side-by-side test, where you pick one winner rather than combining their work into a single output.