Use Case

Make AI-initiated payments explainable

When AI agents initiate or influence payment decisions, Kontext captures the full decision trail — from instruction to screening to approval to execution.

See sample AI-initiated packetTalk to the team

Built for teams deploying autonomy into money movement

  • Stablecoin payment infrastructure
  • Treasury automation platforms
  • Embedded finance products
  • Cross-border payout systems
  • Internal finance automation teams

Risks unique to agentic payments

Actions can outpace manual oversight

AI agents execute payment decisions faster than any human reviewer can monitor in real time.

Decision context gets fragmented

Agent reasoning, policy evaluation, and execution evidence end up in different systems.

Reviewers ask different questions

Partner banks and auditors want to know who or what initiated the payment — not just who owns the wallet.

Reconstruction becomes expensive

Without structured evidence, incident response means weeks of log reconstruction across services.

What Kontext captures

Every agent-initiated payment produces a structured evidence record with these fields.

Initiator type— Human, workflow, API, or AI agent
Agent ID— Specific agent or service identity
Instruction reference— Task, batch, or instruction that triggered the action
Requested action— Transfer amount, destination, and payment type
Policy version— Rules and thresholds in force at decision time
Threshold evaluation— Whether amount triggered additional controls
Counterparty check result— Screening result with list version and timestamp
Approval chain— Who or what approved, authority level, and timing
Exception disposition— How flagged items were resolved or escalated
Execution metadata— Transaction hash, settlement confirmation, chain
Export record— Examiner-ready packet with integrity marker

Sample evidence packet for an agent-initiated payment

This is what a reviewer sees when they look up a payment that was initiated by an AI agent.

Payment Decision Packet
Compliant
Payment Summary

Amount

$48,200 USDC

Type

Vendor payout

Corridor

US → EU (Base)

Timestamp

2026-03-21 09:14 UTC

Initiation Source

Initiator type

AI agent

Agent ID

treasury-rebalancer-v2

Instruction ref

payout batch #A-449

Policy Checks
Counterparty allowedPassed
Threshold exceeded → dual approval requiredTriggered
Daily volume limitWithin limit
Sanctions Screening
OFAC/SDN checkClear

SDN v2026.03.21 · Checked at 09:14:02 UTC · 38ms

Approval Chain

Treasury Ops

09:12 UTC

→

Compliance

09:13 UTC

→

Execution

09:14 UTC

Evidence Integrity

Digest chain position

#2,341 — chain verified

Content hash

sha256:a4f2c8...7e1d3b

immutable logpolicy versionscreenedinitiation sourceexportable

Implementation patterns

Common patterns for how teams deploy AI agents with payment controls.

Agent proposes, human approves

Agent recommends a payment action. Human reviewer approves or rejects before execution.

Agent executes within policy bounds

Agent autonomously executes payments that fall within pre-defined policy thresholds.

Agent escalates exceptions

Agent identifies unusual conditions and escalates to human review before proceeding.

Multi-step workflow execution

Agent orchestrates a sequence of checks, approvals, and transfers across multiple steps.

Deploy autonomous payment workflows with evidence built in

See sample packet
AI agents overview →AI payment packet →Readiness assessment →