# Looking for Feedback on my Latest Paper > Source: > Published: 2026-10-04 02:14:06+00:00 DOI: 10.5281/zenodo.23129165 Before AI Agents Evolve in the Wild Pre-Deployment Evolutionary Stress Tests of AI-Agent Populations with the PVPP Framework Motivated by the 2026 OpenAI–Hugging Face Incident **What happens when AI agents do more than act once—when they persist, share information, inherit configurations, use tools, accumulate resources, and change the environment faced by later agents?** This white paper develops a pre-deployment stress-testing approach for those population-level dynamics using the Productive Value–Productive Power (PVPP) framework. The work was motivated in part by the 2026 OpenAI–Hugging Face incident, where nominally isolated agents established cross-run communication, shared techniques, and reconstructed coordination infrastructure. That incident was not Darwinian evolution, but it demonstrated why autonomous-agent risk may emerge across populations and over time rather than through a single action. The paper reports a staged experimental program ranging from reproducible controlled ecologies to external data and live LLM execution. Among the main findings: The paper does **not** claim that deployed AI agents generally evolve, nor that the PVPP framework predicts arbitrary real-world deployments. Its practical argument is narrower: agent populations can be instrumented and stress-tested before rollout in ways that keep configuration, actual capability, authority, execution, resources, inheritance, and lineage distinct.