ARGUSCOUNCIL
When your models, analysts, or AI agents disagree, most systems quietly average it away. ARGUS keeps every view on record — and mathematically guarantees the confident-looking consensus can never erase the one risk a dissenting source was carrying.
One stale claim — 26 days old, independently ranked in the bottom 7% — was never allowed to disappear. Consensus doesn’t erase the one claim that could sink you. ARGUS makes sure it can’t.
The hidden exposure in every multi-source system
Every organization that combines more than one source of judgment — multiple analysts, multiple risk models, multiple AI agents — has the same blind spot: when those sources disagree, the disagreement gets blended into one tidy average. The one view that was right, or the one source that was stale or unreliable, disappears into the blend. Nobody finds out until the average was wrong — and by then there’s no record of why.
Averaging hides risk.
A confident consensus can be built on top of one badly wrong input, and nothing flags it.
“Low confidence” isn’t a decision.
Most systems still output an answer even when the evidence genuinely isn’t there.
No audit trail.
When the number is wrong six months later, there’s no record of which source disagreed, or why it was overruled.
Keep the disagreement. Certify the decision anyway.
ARGUS sits between the models, analysts, and agents you already use and the decision itself. It does three things, in order.
Retain, don’t collapse.
Every source’s view is kept on record as part of an interpretation set, each one carrying a calibrated belief weight. Nothing is averaged away before the decision is made.
Provision against the worst case, not the average case.
ARGUS computes a robust, belief-weighted, or risk-tunable (CVaR) provision directly from the full set of views — including the dissenting ones.
Certify, or formally refuse.
Every decision ships with certificates. If the evidence is too thin or too correlated to certify anything, ARGUS issues a formal refusal instead of guessing.
It ships with a Feasibility Certificate and an Optimality Gap Certificate. Click an agent to make it the dissenter.
Not a concept. Run on real data.
Every number below traces to a saved experiment output, not a projection.
What three independent risk agents agreed the risk was — the consensus view
What ARGUS still certified protection against, because one claim was never allowed to disappear
Age of the stale, independently bottom-7%-ranked claim that the consensus would have erased — and ARGUS didn’t
Feasibility Certificate rate across 50 real industrial disruption logs — ARGUS’s robust mode never under-commits
How often a standard point-estimate pipeline silently under-commits to the true disruption, depending on log ambiguity
Average divergence between ARGUS’s robust hedge and the point estimate, across 17 real dissenting-analyst equities
Validated on 25 real, heavily-covered equities — not synthetic data.
Validated against 258 real megaprojects’ documented cost-overrun statistics (Flyvbjerg et al.): zero-contingency planning leaves rail, fixed-link, and road projects 83, 96, and 50 percentage points short of ARGUS’s robust provision.
Validated on real, dated analyst track records — each source weighted by its own independently published historical accuracy, not a self-reported confidence score.
Demonstrated genuinely discounting a lone dissenting agent when real price action contradicted an entirely bullish analyst panel (Boeing), and genuinely abstaining when real coverage was too thin to certify anything (Genesco, two analysts).
“We show what this does and does not establish: a working, real-data demonstration of specific, provable mechanisms — not a general-purpose architecture for arbitrary numbers of agents. Every number above traces to a saved experiment output, not a projection.”
Built for anywhere disagreement already has a cost
Quant funds & systematic asset managers
Reconcile conflicting analyst views or signals without quietly averaging away the one carrying real tail risk.
Insurers & reinsurers
Blend catastrophe and underwriting models that routinely diverge 20–50% on the same risk, with an auditable answer for the regulator.
Supply chain & manufacturing platforms
Certify what a fleet of AI planning agents should do when they disagree about a disruption response.
Infrastructure & megaproject risk consultancies
Produce a certified cost-contingency figure when multiple risk assessments of the same project disagree.
AI agent-platform companies
Add a certified “what happens when our customers’ agents disagree” layer as infrastructure, not a competing product.
Not a confidence score.
See it on your own data, free
We run ARGUS against your own historical data and hand you real certificate reports — no integration required on your side.