Simulated User
person concept tool org talk claim — click a node to jump to its page; hover an arrow for the relation
A Simulated User is a synthetic stand-in for a human participant in an agent interaction, implemented as an LLM driven by its own prompt rather than a live human providing input.
How It Works
Rustem Feyzkhanov (Snorkel AI) describes the pattern directly: rather than requiring a real user to interact with an agent during testing or evaluation, "you can simulate the user. In this case, that becomes effectively LLM with its own prompt." (9:21) This makes the simulated user a peer component within the broader agent system — an LLM-backed role that generates inputs, responses, or queries in place of a human counterpart.
Role in Agent Simulations
Feyzkhanov advocates for this technique in the context of moving from agent traces to full agent simulations. By replacing the human side of a conversation with a prompted LLM, it becomes possible to run end-to-end agent interactions autonomously — enabling scalable testing, evals, and data generation without human-in-the-loop overhead. (9:21)
Points of Disagreement
No dissenting views on the Simulated User concept appear in the current material.