Merchant Network Trust Intelligence: Shared Signals for Agent Risk
Why isolated fraud models fail in agentic commerce
The Isolation Problem
Every merchant sees a slice of agent behavior. One team flags an agent for suspicious velocity, another approves the same agent because their order looks clean. The result is inconsistent policy and missed fraud signals.
Agentic commerce compresses timelines: a single agent can touch dozens of merchants in minutes. Siloed fraud models cannot learn fast enough to keep up.
If each merchant operates alone, the network learns too late.
What Merchant Network Trust Intelligence Does
Merchant Network Trust Intelligence (MNTI) is a shared signal layer that lets verified merchants exchange anonymized risk indicators about agents and transactions.
- Early warning alerts when agents trip suspicious thresholds elsewhere.
- Collaborative blocklists for clearly malicious agents.
- Benchmarking against peer merchants to tune thresholds.
How Signals Flow (Without Exposing Data)
Signals are shared at the agent and behavior level, not the customer level. Merchants submit anonymized indicators—such as velocity spikes, policy violations, or mismatched authority scopes.
Anonymized agent identifiers
Hashes and signed agent IDs prevent sharing sensitive customer data.
Scoped event types
Merchants choose the signal granularity they want to share.
Auditability by design
Every signal is logged so merchants can review how it shaped a decision.
Why This Matters Now
The moment agents move from experimentation to production, the cost of shared intelligence drops and the cost of isolated decisions spikes.
MNTI gives merchants a way to keep approvals high while still reacting to new fraud patterns.
Shared intelligence turns agent commerce from reactive to resilient.
Build the Shared Signal Layer
Join the network shaping how merchants exchange risk intelligence for agent commerce.
Talk to the KYA Team