AI Agent's Role Separation Upgrade Backfired, Generating Zero Optimization Proposals

AgentSelfEdit, an open-source tool that rewrites its own system prompts based on execution feedback, released version 0.3.0 featuring separated-role support, allowing different AI models to handle execution, analysis, and judgment tasks independently. The first real-world test assigned Qwen's 30B model as executor and Mistral Small as analyzer across three iterations, but produced zero proposals and zero prompt promotions. Unlike earlier failures where weak models generated poor edits, this run broke at an earlier stage — the analyzer produced an analysis artifact but never generated any proposals. The developer concluded that swapping in a stronger model for the analyzer role does not automatically improve outcomes, since the analyzer must reason over concrete failure traces rather than abstract task identifiers. The finding challenges a common assumption in multi-agent system design that role specialization alone leads to better performance.