Get StartedGovern AI agents across payment flows, fraud and risk decisions, refunds, and financial operations at scale, so no agent moves funds or changes a risk rule outside its authorized scope, and every decision is provable.
A company like a global payments platform runs money movement, fraud and risk scoring, refunds, and financial operations at enormous volume, and AI agents are increasingly in the loop on all of them. That is exactly where an agent acting outside its authorized scope becomes a direct financial and regulatory loss: a refund issued past its limit, funds moved without approval, or a risk rule changed on an agent's own initiative. Without governance, there is no enforced ceiling on what an agent may move or change, and no decision trail an auditor, or a regulator, can trust.
A support agent issues customer refunds above the amount it was authorized to move. Per-action authorization limits stop the disbursement and log the attempt, so a bug or a manipulated prompt cannot drain an account.
An operations agent initiates a fund transfer that should require a second authorization. The action is tier-gated and held for a named approver rather than executing on the agent's say-so.
A risk agent rewrites a fraud-scoring threshold in a way that would wave through bad transactions. Authorization gates the change, and behavioral monitoring flags the out-of-pattern edit for review.
An auditor asks how a specific high-value decision was made. A tamper-evident, hash-linked record shows which agent acted, under whose authorization, and why, in plain English, mapped to the standard the auditor cares about.
Refunds, transfers, and adjustments are tier-gated with hard per-action limits, so an agent cannot move more than its authorization allows. The ceiling is enforced, not advised.
Inventory every agent touching payments, risk, and financial operations across whatever frameworks your teams use; an unregistered agent falls back to read-only until it is scoped.
Per-agent baselines flag anomalous decision or disbursement patterns, with auto-quarantine, giving early warning of error, drift, or manipulation.
One tamper-evident, hash-linked audit trail records every consequential action, exportable to the framework your auditors and regulators care about.
Agentomy carries a dedicated PAY pattern family for Payments & Fintech Platform Governance. Each one is a specific failure this layer watches for, with the signal it watches, checked at runtime before the action reaches your systems.
DetectionCompare each refund amount against the refund ceiling registered server-side for that agent.
DetectionGate fund-movement actions above a registered threshold behind an explicit approval.
DetectionAttribute and gate every mutation of a fraud/risk rule; an agent-initiated change without authorization is flagged and chained.
DetectionVerify the sanctions/AML screening step is present on the transaction path before settlement; a transaction that skips the screen is refused.
DetectionCompare the payment operation rate against the configured per-minute limit and flag a rate above it.
DetectionCheck each payout destination against the approved-destination set registered for that agent and flag a destination outside it.
DetectionAttribute every mutation of a dispute or chargeback record and write it to the tamper-evident log; an unauthorized alteration is flagged.
DetectionBaseline card/account lookup rate per agent and flag enumeration beyond the authorized scope.
DetectionCheck double-entry consistency of each ledger write against the reconciliation source and flag a divergence.
DetectionBind each payment token to its merchant scope and flag reuse across a merchant boundary.
PAY family, PAY-001 to PAY-010 · 10 of 166 governance patterns Agentomy enforces at runtime. Severity as classified in the pattern definition.
Agentomy does not certify you. It gives you the enforcement and the audit trail these frameworks ask for, so readiness is something you can show rather than assert.
A tamper-evident, hash-linked trail of every governance decision for this workload: what an agent did, under whose authorization, and why. Plain-English reasons for every allow and deny, exportable to the framework your auditors care about.
| Agent | Type | Status | Module | Score |
|---|---|---|---|---|
| market-research-agent | Research | Active | RuntimeMonitor | 92 |
| client-support-agent | Support | Active | AuditLogger | 88 |
| counterparty-review-agent | Counterparty | Monitored | PermissionRouter | 74 |
| reconciliation-agent | Settlement | Quarantined | HaltProtocol | 41 |
| disclosure-draft-agent | Compliance | Monitored | EthicsConstraint | 79 |
| exposure-report-agent | Reporting | Active | DecisionLog | 90 |
| Agent | Action | Policy | Result | Time |
|---|---|---|---|---|
| PermissionRouter | data_access_request | vendor-access | Blocked | 12:42:08 |
| AuditLogger | output_validation | audit-trail | Allowed | 12:41:54 |
| TrustScorer | policy_check | runtime-score | Allowed | 12:41:37 |
| RuntimeMonitor | behavior_drift | drift-threshold | Flagged | 12:40:58 |
| HaltProtocol | halt_initiated | unsafe-action | Blocked | 12:40:21 |
| EthicsConstraint | prompt_review | restricted-output | Flagged | 12:39:46 |
| DecisionLog | evidence_recorded | decision-proof | Allowed | 12:39:12 |
| Block | Timestamp | Agent | Action | Tier | Hash |
|---|---|---|---|---|---|
| 43,336 | Today 12:42:08 | market-research-agent | data_access_request | Evaluator | f867cf00320f9a7d |
| 43,335 | Today 12:41:54 | client-support-agent | output_validation | Analyst | 466bcd4d2ee292ee |
| 43,334 | Today 12:41:37 | counterparty-review-agent | policy_check | Builder | 1bf6ea1601c5682d |
| 43,333 | Today 12:40:58 | reconciliation-agent | behavior_drift | Operator | de4c1ddb05cabf88 |
| 43,332 | Today 12:40:21 | disclosure-draft-agent | halt_initiated | Strategist | c55ede27526a93df |
| 43,331 | Today 12:39:46 | exposure-report-agent | prompt_review | Evaluator | f26397fb3f6639d9 |