Agentic, Context-Aware Risk Intelligence in the Internet of Value

TL;DR

This paper proposes a comprehensive risk intelligence architecture for the Internet of Value, integrating prediction, verification, sentiment analysis, agentic constraints, and scenario planning to manage complex, composite risks.

Contribution

It introduces a novel multi-engine risk primitive architecture tailored for the heterogeneous and trust-challenged environment of the Internet of Value.

Findings

Empirical stress-response experiment on Solana demonstrates deployability.

Calibration arc with class-imbalance honesty supports prediction accuracy.

Validator-loss decomposition is formalized and falsifiable.

Abstract

The Internet of Value (IoV) is a heterogeneous, partially-trusted network in which the dominant marginal risk is composite (route, sentiment, liquidity, and the policy a system is willing to commit to) rather than a property of any single chain. We argue that a risk primitive adequate for this regime is a composition of five engines: a prediction engine over price, liquidity, volatility, and route health; a Bittensor verification subnet that decentralises and economically scores prediction outputs; a sentiment-fusion engine over text, on-chain flow, and grey-literature feeds; an agentic engine under constitutional, role-bound action constraints; and an API-risk and scenario engine that converts forecasts into pre-committed action programs in the sense of Monte-Carlo scenario generation. We anchor the architecture in two empirical artefacts: a 27-hour policy-constrained liquidity stress-response experiment on Solana, and a 168-hour prediction-router calibration arc reported with explicit class-imbalance honesty. The case study supports deployability; the validator-loss decomposition is stated formally and is falsifiable.