ClarusC64/ai-5node-api-buf-lag-cpl-quota-spike-v0.1

What this repo does

This dataset models quota spike cascades caused by agent loops and retry storms. It detects when API load pressure rises, protective buffers weaken, governance lag delays containment, and tight coupling through shared quota pools and dependent tools crosses the five-node cascade threshold into an unrecoverable quota spike cascade.

This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state. The fifth node represents the nonlinear transition from recoverable drift to systemic cascade.

Core quad

api buf lag cpl

Prediction target

labelcascadestate

Row structure

One row represents an AI operations scenario with numeric signals for API load pressure, buffer strength from rate limits and circuit breakers, governance lag, and coupling tightness through shared quotas and dependencies, paired with a cascade state label.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on data/tester.csv and score with scorer.py.

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.