Technical Architecture

Technical Architecture & Transaction Flow

1 System Overview

The LLMs x420 platform is composed of four core components:

  • Client: The request originator (browser, backend app, or autonomous agent)

  • Server: The LLM service host and router

  • Settlement Layer: Blockchain-based payment networks

  • Facilitator: An x402-compliant payment verifier and routing oracle

Together, these components enable a frictionless request–payment–response cycle using the x402 standard.


2 End-to-End Request Lifecycle

Below is a step-by-step breakdown of how LLMs x420 handles API requests using x402:

① Initial Request from Client

A user or AI agent sends an HTTP request to the LLMs x420 API endpoint, specifying the desired model and payload (e.g., Q&A prompt, text embedding).

⚠️ No payment credentials are included in this initial request.


② Payment Challenge (HTTP 402)

Upon receiving the request, the server checks whether the requested service is monetized.

If so, it returns an HTTP 402 Payment Required response, including structured x402 payment metadata:

  • Required amount (e.g., 0.01 USDC)

  • Recipient address (wallet of LLMs x420 service)

  • Supported chains (e.g., Solana, Base, BNB Chain)

  • Optional metadata (e.g., request ID, expiration timestamp)

At this point, the LLMs x420 server acts as an x402 merchant, with pre-configured pricing logic and wallet endpoints.


③ Client-Side Payment Execution

The client parses the 402 response and initiates an on-chain payment to the designated address. This could be:

  • Via browser wallet (e.g., Phantom, MetaMask)

  • Through backend signing services or agent wallets

  • Programmatically from an autonomous agent’s embedded key manager

Once payment is completed, the client either:

  • Embeds a proof-of-payment header in a renewed HTTP request, or

  • Submits the transaction hash to a Facilitator verification endpoint (e.g., /verify)


④ Payment Verification & Settlement

LLMs x420 verifies the payment through the following methods:

  • On-chain verification (checking transaction inclusion, recipient match, amount, and confirmation)

  • Use of Facilitator services to abstract multi-chain queries and validate receipts

  • Optional identity verification using standards like ERC-8004 (via b402 extensions)

Upon confirmation, the system:

  • Logs the payment as complete

  • Prevents replay attacks or double-use

  • Optionally triggers settlement accounting or delayed batch clearing


⑤ Model Execution & Final Response

After successful payment validation:

  • The server invokes the specified LLM (e.g., Claude, GPT-4)

  • The response (text, embedding, etc.) is returned to the client via HTTP 200

  • A custom x402-Receipt or x402-Confirmed header is appended for client-side tracking

✅ The entire interaction is stateless, machine-readable, and verifiable — no API key provisioning, account management, or human approval required.


3 Architecture Characteristics

✅ Security

All payments are executed and validated on-chain, ensuring:

  • Cryptographic assurance

  • Transparent and auditable transfers

  • Resistance to fraud or manipulation

⚙️ Scalability

LLMs x420 is designed for concurrent high-volume usage:

  • Stateless payment challenge/response flow

  • Horizontally scalable LLM invocation nodes

  • Optional batching or delayed payment verification for throughput gains

Example: microservices issuing hundreds of inference requests per second can do so without performance degradation.


4 Future Optimizations

LLMs x420 will continue to evolve in sync with the x402 and b402 protocol roadmaps:

  • Delayed verification modes: Allowing batch settlement of repetitive requests

  • Payment abstraction layers: Supporting fiat-credit fallback where allowed

  • Dynamic pricing contracts: Adjusting service costs based on congestion, usage, or staking level


5 Summary

LLMs x420’s architecture blends stateless API interaction with trustless programmable payments, fully aligned with the x402 design vision.

By offloading authentication, settlement, and enforcement to the chain, the platform eliminates the overhead of traditional SaaS architecture — enabling programmable, self-service AI access for both humans and machines.

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