Why do AI agents miss most of what users actually want?

UserBench evaluates agents in multi-turn, preference-driven interactions where simulated users start with underspecified goals and reveal preferences incrementally. The results quantify a gap that existing benchmarks obscure:

• Models provide answers that fully align with ALL user intents only 20% of the time on average
• Even the most advanced models uncover fewer than 30% of all user preferences through active interaction
• Scores drop by over 40% when models must select only one option per dimension (forcing commitment rather than hedging)

The framework identifies three core traits of human communication that make this hard:

1. Underspecification — users initiate requests before fully formulating their goals
2. Incrementality — intent emerges and evolves across interaction turns
3. Indirectness — users obscure or soften their true intent due to social or strategic reasons

These are not edge cases — they are the default condition of human communication. Language is inherently ambiguous (Clark, 1996; Liu et al., 2023), and meaning is co-constructed through interaction.

The disconnect between task completion and user alignment is the critical finding. Standard benchmarks measure whether an agent completes a task — UserBench measures whether the agent completed the right task, from the user's perspective. Current models are task-capable but not user-aligned.

This connects to Why can't users articulate what they want from AI? — the 20% figure quantifies the double gap. And since How do users actually form intent when prompting AI systems?, the incrementality trait confirms that intent-as-binary is a design error, not an edge case.

The finding that models elicit <30% of preferences through active querying connects to Can models learn to ask clarifying questions instead of guessing? — proactive questioning is trainable (0.15% → 73.98%) but is not standard in current deployments.