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.
No signup. Analyses take 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 — ads, viral claims, "educational" articles, income-guru pitches — across eleven credibility dimensions: factual plausibility, scientific consensus, source integrity, emotional manipulation, hidden agendas, advertorial funnel architecture, and more. The result is an auditable credibility profile, not a single opaque score, applied symmetrically across all political, commercial, and ideological perspectives.
cca.eigenagents.ai →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.