@ericyocam6Shipped: 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.
Comments2
Publishing that neither real AI-agent graph passed your antifragility test is a useful result, since most teams wiring agents together assume the system holds up when one piece breaks. Will you test a company's live agent setup next?
Thanks, Ryan. I agree. One of the more interesting findings was that neither AI-agent graph demonstrated strict antifragility, which highlights the difference between resilience and actually improving under disruption. Testing a company's operational multi-agent environment would be a logical next step, particularly to evaluate how agent failures, communication disruptions, and changes in network topology affect overall system performance. I would be interested in collaborating with organizations willing to share anonymized agent interaction data or provide access to a controlled testing environment. Appreciate the suggestion!
2 comments
Publishing that neither real AI-agent graph passed your antifragility test is a useful result, since most teams wiring agents together assume the system holds up when one piece breaks. Will you test a company's live agent setup next?
Thanks, Ryan. I agree. One of the more interesting findings was that neither AI-agent graph demonstrated strict antifragility, which highlights the difference between resilience and actually improving under disruption. Testing a company's operational multi-agent environment would be a logical next step, particularly to evaluate how agent failures, communication disruptions, and changes in network topology affect overall system performance. I would be interested in collaborating with organizations willing to share anonymized agent interaction data or provide access to a controlled testing environment. Appreciate the suggestion!