Independent research built around falsifiable failure mechanisms

AI is no longer one model. Agents, orchestrators, shared memory, external tools and autonomous decision chains create failure modes in which one error can propagate, become apparent consensus and reach action before its origin is understood.

ICI tests the decision chain rather than only the final output. It keeps observed, inferred, verified and remembered states separate, challenges causal explanations, looks for hidden dependencies between agents and sources, and checks whether a correction survives changed conditions.

In an orchestrated multi-agent system, five agents may agree while all depend on the same source, memory object or initial assumption. ICI asks whether convergence is truly independent and whether the conclusion survives when the common dependency is removed or changed.

ICI is being developed as an assurance method that is not tied to one model architecture. The research roadmap extends controlled testing toward hybrid and quantum-assisted decision systems, where larger decision spaces make evidence provenance, causal stability and retained error mechanisms even more important. This is a research direction, not a claim of production quantum capability today.