The world expects $4.6 trillion in agent-driven transactions by 2030. We're building the infrastructure to make that possible — real-time decision quality verification so humans stay in control as AI agents move money.
The agentic economy is accelerating. Autonomous AI agents will negotiate, purchase, and allocate capital without human intervention at the point of sale. But adoption stalls without trust infrastructure — and the humans who deploy these agents need guarantees, not guesses.
Card networks and payment rails were never built for non-human actors. Frontier labs build reasoning capabilities — we build the decision verification layer that makes those capabilities safe for commerce. We score agent decision quality so humans never lose control of what their agents spend.
We accelerate adoption — but with trust and safety.
Our research is focused on understanding how AI agents make economic decisions, and how transaction data can train better models for decision quality scoring and behavioral telemetry.
A metric we are developing to optimize orchestration mechanisms in the deployment of specialized procurement agents. EDQS quantifies the quality of an agent's economic reasoning in real-time — enabling platforms to route, throttle, or halt transactions based on decision integrity.
Analysis of transaction data used as a base model of reference for agent behavioral patterns. By studying how agents behave across millions of decision points, we build the foundational models that detect decision drift, mandate violation, and reasoning degradation.
Our research directly informs our product — understanding how AI agents make economic decisions is foundational to building real-time decision verification infrastructure.
The next evolution of financial verification. Just as Know Your Customer became the foundation of modern compliance, Know Your Agent establishes decision quality scoring for autonomous commerce.
Real-time behavioral health assessment. Continuous monitoring of agent decision state during transaction flows — detecting drift, confusion, and manipulation before value transfers.
Every transaction requires declared reasoning. Agents must articulate why they are making a purchase decision, creating an auditable chain of intent that humans can review.
Spending boundaries with adaptive gates. Principal-defined constraints that respond to real-time decision quality scores — tightening when agent behavior deviates from baseline.
Decision quality scoring and behavioral telemetry for every participant in the agentic economy. Giving humans the tools to trust — and control — their autonomous agents.
Monitor agent purchasing behavior across your card portfolio. Detect decision drift before chargebacks happen.
Deploy specialized purchasing agents with verifiable decision quality. Every procurement decision is scored and auditable.
Give your agent marketplace a trust layer. Users know which agents maintain decision integrity over time.
Autonomous trading agents with real-time decision health monitoring. Halt execution when decision quality degrades.
Integrate decision quality scoring into your authorization flow in minutes. Sub-100ms responses, event-driven architecture, full sandbox environment.
Python + Node.js
Sub-100ms authorization decisions
Event-driven architecture
Full testing environment
# Authorize an agent transaction with decision verification from mandate import KYA client = KYA(api_key="mk_live_...") authorization = client.authorize( agent_id="agent_9f3k2", transaction={ "amount": 2400, "currency": "usd", "merchant": "cloud_provider", "declared_intent": "Scaling compute for batch inference job #847" } ) # Returns: decision_quality_score, verified, reasoning_audit
We are building the foundational rails for decision trust. Issuers, processors, and platforms integrate our verification layer into existing authorization flows — giving humans the power to safely control agentic activities at scale.
The KYA standard. An open specification for agent decision verification that any participant in the value chain can implement.
Transaction data analysis that builds reference models for agent behavior — enabling real-time anomaly detection and decision quality scoring.
Sub-100ms decision verification in the authorization path. Every agent transaction scored before money moves.