Evaluating the Portability of Structured AI Agent Identities Across Language Models

This article explores the concept of 'cross-model persona fidelity' - the degree to which an AI agent's behavior and identity remain consistent when the underlying language model is swapped. The author outlines five key dimensions of fidelity and describes an experiment comparing agent performance across four different language models.

Details

The article introduces the concept of 'cross-model persona fidelity' - the degree to which an AI agent's behavior and identity remain consistent when the underlying language model is swapped. This is an important but untested promise of AI agent standards like Soul Spec and CLAUDE.md, which assume the agent's 'persona' is portable across models. The author outlines five key dimensions of fidelity to evaluate: identity consistency, tone alignment, memory utilization, behavioral rule compliance, and task accuracy. An experiment is described that tests these dimensions across four different language models, including commercial APIs and open-source models. Expected failure modes include safety-induced personality suppression, persona drift under complexity, and capability-fidelity tradeoffs. Successful cross-model fidelity has implications for vendor independence, system resilience, and the viability of open-source language models for persona-driven agents.