Trust Over Fear: How Motivation Framing in System Prompts Affects AI Agent Debugging Depth
This addresses the problem of optimizing AI agent behavior for debugging tasks, showing that motivational framing can causally influence investigation depth, though it is incremental in exploring prompt design effects.
The study investigated how trust-based versus fear-based motivation framing in system prompts affects AI agent debugging performance, finding that trust-framed agents found 59% more hidden issues in one study and 25% more in another, while fear-based framing showed no significant improvement.
System prompts for AI coding agents increasingly employ motivational framing -- from neutral task descriptions to fear-driven threats -- yet no controlled study has examined whether such framing affects agent behavior. We present two studies investigating how trust-based versus fear-based motivation framing in system prompts influences AI agent debugging performance. In Study 1, we conducted a controlled manual experiment comparing a trust-framed methodology (NoPUA) against an unframed baseline across 9 debugging scenarios using Claude Sonnet 4. Trust-framed agents found 59% more hidden issues (p = 0.002, d = 2.28) while taking 83% more investigative steps, despite finding 15% fewer surface-level issues -- revealing a depth-over-breadth tradeoff in investigation strategy. In Study 2, we replicated and extended these findings with 5 independent automated runs across 3 conditions (Baseline, NoPUA trust-framed, PUA fear-framed), yielding 135 scenario-level data points. Trust-framed agents again showed significant advantages: +74% investigative steps (p = 0.008) and +25% hidden issues found (p = 0.016). Crucially, fear-framed (PUA) agents showed no significant improvement over baseline on any metric (all p > 0.3), demonstrating that fear-based motivation is ineffective for AI agents. We ground these findings in Self-Determination Theory, regulatory focus theory, and satisficing models, arguing that trust-based framing induces exploration-oriented, promotion-focused behavior while fear-based framing fails to shift agents from default satisficing strategies. Our results suggest that the motivational frame of system prompts -- not just their technical content -- causally influences AI agent investigation depth.