PAYMNTS sits between AI agents and payment rails — verifying mandates, scoring risk, screening sanctions, and writing the audit trail for every agent-initiated transaction. Before the money moves, not after the incident.

Six independent systems, one verdict — so no single failure lets an agent run wild.
Every transaction must cite a live, signed mandate: amount cap, merchant scope, expiry. Violations hard-decline in strict mode — not "warned", declined.
Mandate compliance, sanctions proximity, behavioral anomaly, merchant risk, and velocity exposure — fused into one score with evidence attached.
Counterparties screened by name, alias, and graph proximity before approval.
Each agent builds a normal-behavior profile over time — anomalies surface as scored deviations, not guesses.
Suspicious Activity Reports generated from real evidence: facts, scores, graph paths, audit trail.
Every decision carries its evidence chain — who mandated, what scored, why it cleared. Auditors query the trail directly.
Agents verify mandates and check risk natively in their own flow; your team reviews escalations in the console.
Compliance overrides beat weighted scores — sanctions hits always stop the money.
Every check, verdict, and override streamed live — you see system health before your customers feel it.
Agent gets a graph identity bound to an owner. The owner signs a mandate: amount cap, merchant scope, expiry — cryptographically bound, tenant-isolated.
Via ACP, Agent Pay, VIC, or AP2, the event normalizes into our pipeline — mandate check, sanctions screen, behavioral analysis, velocity check.
Approve, escalate, or decline — with scored evidence and a 7-year audit trail. Regulators get provenance, not a shrug.
Wherever an AI agent can initiate a payment, there's a compliance question someone will ask. These are the teams who'll hear it first.
PSP routers and checkout platforms score agent traffic before choosing a rail — clean agents take the fast path, risky ones get stepped up.
Agent-provisioned cards bound to a mandate — spend cap, merchant scope, and expiry enforced per-authorization, not just per program.
Agent-initiated sweeps, transfers, and reconciliation — every movement scoped to a signed mandate with an audit trail regulators can read.
Agents driving wallet operations get screened beyond policy scope — counterparty graph proximity catches what allowlists can't.
Agent buyers purchasing on behalf of users — mandate-verified orders with evidence that settles "who authorized this" disputes instantly.
Risk engines built for human telemetry get the missing agent signals — mandate compliance, behavioral deviation, counterparty context — as a feed.
Same scoped, rate-limited assurance engine behind every door: REST for services, MCP for agents, OpenAPI for typed SDKs.
# score a transaction before it settles
curl -X POST "https://YOUR-HOST/api/v1/pymnts/risk/score?tenant_id=tenant_acme&project_id=project_payments" \
-H "x-api-key: YOUR_KEY" \
-H "content-type: application/json" \
-d '{
"tx_id": "tx_9f2", "agent_id": "checkout-bot",
"amount": "129.99", "currency": "USD",
"merchant_id": "acme-store", "protocol": "acp"
}'
Scoped by tenant + project. Every response carries
X-RateLimit-* headers for your tier.
// claude_desktop_config.json / any MCP client
{
"mcpServers": {
"paymnts": {
"url": "https://YOUR-HOST:8121/mcp",
"headers": { "x-api-key": "YOUR_KEY" }
}
}
}
verify_mandate · score_risk ·
check_sanctions · log_transaction ·
query_agent_history · check_merchant
# generate a typed SDK from the live spec pip install openapi-python-client openapi-python-client generate \ --url https://YOUR-HOST/openapi.json
The spec is the contract — always in sync with the deployed routes, never hand-drifted.
Rate limits scale with your tier — every tier gets the full assurance engine, the difference is throughput and retention.
Badge in and put every transaction under the radar sweep.
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