An eigenvector holds its direction while everything around it transforms. EigenAgents builds multi-agent AI systems with the same property — compact, production-grade pipelines that keep their bearing as models, data, and platforms shift underneath them. Everything on this page is live software, not slides.
Run the live demo No signup. Analysis takes about a minute.An eight-agent pipeline that evaluates a trend, technology, cultural phenomenon, or financial theme against the Novelty Asymptote framework — the point at which a trend's novelty value approaches zero regardless of its objective quality. It scores novelty decay, dilution ratio, incumbent absorption risk, and resurrection probability, then renders a verdict.
Scores any piece of content across eleven credibility dimensions — factual plausibility, scientific consensus, financial realism, source integrity, emotional manipulation, logical coherence, and more — producing an auditable credibility profile rather than a single opaque score. Public demo coming shortly.
Finds patterns that exist in data but have never been articulated by human experts — the sub-articulation lane of data analysis.
Tests causal claims against data and returns a verdict: confirmed, confounded, or reversed within a subpopulation.
Comparative equity ranking through pairwise agent deliberation rather than single-pass scoring.
These agents run against private or client data and are demonstrated on request — vincent@eigenagents.ai.
EigenAgents is the independent practice of Vincent Kowalski — AI architect, data architect, and builder of agentic systems for the energy and enterprise software industries. M.S. in Artificial Intelligence (Johns Hopkins), eight U.S. patents, twenty-five years across energy and chemicals. The operating philosophy: evidence in working code over institutional ceremony.