Shipped: Markovian Antifragility — AI Network Resilience Research

An open-source research framework investigating whether complex networks, including agentic AI systems, can exhibit antifragility—improved stochastic transport performance under targeted disruption rather than simply resisting failure.

The project uses Markovian random walks, graph theory, and controlled edge-damage experiments to evaluate six real-world Stanford SNAP networks and two observed AI-agent interaction graphs. It measures random-walk hitting time, connectivity retention, and global efficiency while comparing preferential attacks against random failures and size-matched controls.

Built as a reproducible Google Colab experiment, the framework analyzes real LLM-agent execution traces and coding-agent tool interactions to explore structural vulnerabilities, topology-dependent resilience, and the limits of apparent antifragility.

Results demonstrate that some network topologies exhibit improved Markovian transport under damage, while neither tested AI-agent graph meets strict antifragility criteria. The research provides a rigorous foundation for evaluating reliability, fault tolerance, and security in interconnected AI architectures.