Knowledge Hub
The 9-layer map of agentic payment infrastructure — from the moment an agent decides to spend, to the moment money settles. Where the gaps are, and who is filling them.
Founder goal
To become the go-to map for the agentic payments stack — where builders, payment companies, and infrastructure teams can understand who is building what, where the protocol gaps are, and what safe agent transactions require.
AI agents are becoming economic actors. I’m mapping the payment infrastructure they’ll need to transact safely.
The 9-Layer Framework
| Layer | What it does | Key Capabilities | Example Players | Agent Value | Gaps / Opportunities |
|---|---|---|---|---|---|
|
1
Intent / DecisionWhere the spend decision is made
→ deep dive
|
Agent determines whether, what, and how much to spend based on context, preferences, and policy constraints. | LLM reasoning, task planning, budget modeling, preference inference |
OpenAI Agents
Anthropic
CrewAI
LangChain
|
Decides whether, what, and how much to spend | ❗ No standard for agent spend policies — LLM budget logic is opaque, non-auditable, and varies by model |
|
2
Identity & TrustWho is acting — human, agent, or bot
→ deep dive
|
Verify agent and user identity, detect automated abuse, and establish trust before payment access is granted. | KYC/KYB, bot detection, device fingerprinting, agent attestation, W3C DID |
Skyfire
KYAPay
Forter
Persona
Socure
|
Establishes trusted identity before payment access is granted | ❗ No universal "proof of agent" standard — existing KYC frameworks were written for natural persons, not software principals |
|
3
AuthorizationWhether this payment is permitted
→ deep dive
|
Policy engine that decides whether a specific payment is permitted — within what limits, under whose delegation, and with what approval chain. | Spend policy rules, delegated authorization, approval flows, 3DS/SCA adaptation |
Visa TAP
MC Agent Pay
AP2
ACP
|
Controls agent spend scope and prevents unauthorized payments | ❗ Platform war in progress — Visa, Mastercard, and crypto protocols competing to own delegation standards; 3DS was built for humans, not agents |
|
4
Payment CredentialsThe instrument the agent uses |
Issue and store the payment instrument the agent actually presents — card token, wallet key, or signed credential. | Tokenization, PCI-scoped vaulting, virtual card issuance, wallet signing |
Nekuda
Basis Theory
Stripe Issuing
Privy
Prava
|
Gives the agent a spendable payment instrument | Most solutions are human-UX virtual card products adapted for agents; no native agent credential spec exists |
|
5
Funding SourceWhere the money comes from |
Connect the agent's spending credential to a real funding source — bank account, card line, or stablecoin treasury. | Bank connectivity, card funding, stablecoin on-ramp, multi-currency treasury |
Stripe
Plaid
Circle
Coinbase
|
Links agent credentials to real money | ❗ Multi-source routing unsolved — agent needs to select between fiat, crypto, and credits in real time; no unified abstraction layer |
|
6
Checkout / ExecutionCompleting the purchase |
The agent completes the purchase — navigating checkout APIs, cart flows, or headless commerce interfaces. | Checkout APIs, cart automation, headless commerce, purchase orchestration |
Stripe Agent Toolkit
Shopify APIs
Browser Use
Rye
|
Translates agent intent into a completed merchant transaction | No agent-native checkout API standard — most flows still require browser automation or custom merchant integrations. |
|
7
Payment RailsHow money moves
→ deep dive
|
The actual movement of money — card networks, bank rails, real-time payments, or on-chain transfers. | Card network clearing, ACH/RTP/FedNow, stablecoin settlement, on-chain transfer |
Visa / MC
FedNow
UCP
Solana Pay
|
Moves value from agent wallet to merchant | Rail fragmentation remains severe — card networks, real-time bank rails, and on-chain systems are still incompatible, with no cross-rail routing layer. |
|
8
Settlement & ReconciliationMoney lands, books close |
Money lands and books close. Automated reconciliation, ledgering, and audit trails for agent-initiated spend. | Ledgering, automated reconciliation, treasury reporting, real-time audit trails |
Stripe Treasury
Modern Treasury
Adyen
|
Closes the financial loop — records spend, triggers reporting | ❗ T+1 card settlement was designed for human-frequency transactions; agent-initiated micro-transactions at high frequency need different clearing windows and reporting hooks |
|
9
Risk / ComplianceWho bears the risk |
Monitor for fraud, enforce AML compliance, manage chargebacks, and assign liability when an agent-initiated payment goes wrong. | AML/BSA monitoring, fraud scoring, chargeback management, liability allocation |
Sardine
Alloy
Unit21
|
Prevents losses, ensures compliance, determines liability | ❗ Visa/MC operating regulations designate the cardholder as a natural person — no dispute or chargeback framework exists for autonomous agent transactions |
Deep Dives
Detailed breakdowns of the layers with the biggest open questions.
Layer 1
Intent / Decision
How agents decide to spend: budgeting, preference modeling, policy constraints, and the missing standard for portable spend logic.
Explore →
Layer 2
Identity & Trust
Who is the agent? Proof-of-agent, KYC/KYB adaptation, and the trust rails that payment systems will require.
Explore →
Layer 3
Authorization
Visa TAP, Mastercard Agent Pay, AP2, and crypto protocols competing to define who can delegate spend and under what conditions.
Explore →
Layer 4
Payment Credentials
The spendable instrument itself: tokens, virtual cards, wallet keys, and the missing credential model designed for software agents.
Jump to layer →
Layer 5
Funding Source
Where the money sits before an agent spends it: bank accounts, card lines, stablecoin treasuries, and cross-source routing logic.
Jump to layer →
Layer 6
Checkout / Execution
How agent intent turns into a completed purchase: APIs, cart flows, browser automation, and merchant-side execution logic.
Jump to layer →
Layer 7
Payment Rails
How money actually moves: card networks, real-time bank rails, stablecoin systems, and on-chain settlement compared.
Explore →
Layer 8
Settlement & Reconciliation
What happens after payment executes: ledgering, treasury visibility, reconciliation, and machine-readable audit trails for agent spend.
Jump to layer →
Layer 9
Risk / Compliance
Fraud, AML, chargebacks, and liability assignment once AI agents begin initiating more transactions on their own.
Jump to layer →