AI agents are beginning to spend money on behalf of humans and organizations. Before this becomes the default mode of commerce, someone has to answer a fundamental question: how do you know an agent is making good economic decisions? We're building the instruments to find out.
You can't trust what you can't verify. Today, there is no standardized way to evaluate whether an AI agent's economic reasoning is sound, aligned with its principal's intent, or operating within acceptable risk. We're changing that.
The financial system was designed around a single assumption: that every transaction has a human principal making a conscious decision. AI agents break this assumption. When an agent autonomously negotiates a price, selects a vendor, or commits a budget, the existing trust infrastructure has no way to verify the quality of that decision in real time.
This isn't a theoretical concern. Agents with access to payment instruments are already operating in production environments. The gap between what they can do and what can be verified about their reasoning is widening every month. Our research addresses this gap directly — building the evaluation frameworks, trust protocols, and verification methods that make autonomous economic activity auditable, governable, and safe.
Developing process-based metrics that evaluate the quality of an agent's economic reasoning independent of outcome — because a good decision can have a bad result, and a bad decision can get lucky.
Designing layered authorization frameworks that let card networks, issuers, and merchants verify agent identity and intent before authorizing transactions on existing payment rails.
Mapping the ways AI economic reasoning can degrade — from Goodhart's Law gaming to adversarial manipulation — and building detection mechanisms that catch problems before they propagate.
We are benchmarking EDQS against transaction-fraud baselines on agent-initiated transactions. Leave your email and receive the results the day they go live.
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