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Unlike human user browser sessions, autonomous AI agents cannot use cookies. You must build a stateless token architecture based on M2M directly. Instead of directly handling cardholder data, you integrate the delegated payment specifications of the agent commerce protocol to eliminate system burden. Using the OAuth 2.1 client credentials flow, commerce authorization tokens are limited to 15 to 60 minutes, while short-term payment delegation tokens are allowed for a maximum of 10 minutes. Using this structure lowers the evaluation items of the PCI DSS v4.0.1 security audit from SAQ D to SAQ A, shortening the audit passing period by two weeks.
To prevent forgery and tampering requests from external agents, you must build an RFC 9421 HTTP message signing middleware. When an agent sends a payment request, it is forced to sign the body hash and timestamp with an Ed25519 private key. The backend gateway executes three lines of defense in order. First, if the signature timestamp tolerance exceeds 60 seconds, it is rejected with 401 Unauthorized. Second, the request's unique nonce is stored in Redis for 8 minutes to prevent replay attacks. Third, public IP whitelisting and web bot authentication signatures are applied to block abnormal scrapers.
Large language models hallucinate when reading unstructured HTML pages or ambiguous API fields. You must expose structured JSON Schemas with clear meanings. When building the catalog API, follow three rules. First, to prevent floating-point calculation errors, all unit prices must be represented in the minimum currency unit as an integer in KRW, and the currency unit must be enforced. Second, instead of letting agents arbitrarily combine parent product IDs and options, flatten the final orderable units into unique SKUs. Third, specify inventory status enumerations and maximum order quantities instead of simple boolean flags. This structure reduces the agent's product information misreading rate to zero percent.
To prevent agents from spamming polls and destroying database I/O, implement HTTP conditional requests and caching layers. Issue hash values that update only when catalog data changes as ETag headers. When an agent queries again with the If-None-Match header and there are no changes, return 304 Not Modified without a body. Additionally, configure cache-control headers on the API gateway and CDN edge nodes to handle short-term caching. When an exception occurs, send an error body following the RFC 9457 problem details format, including a non-retryable flag and the current valid unit price, prompting the agent to immediately deliver precise natural language feedback to the user.
Since an agent's internal reasoning is probabilistic, it would be disastrous to blindly trust and approve the final payment amount thrown by the client. The backend ignores the total amount sent by the agent, receives only the list of order target SKUs and quantities, and recalculates the amount based on the server's internal database master data. When writing the server-side transaction integrity verification middleware, ensure that quantities are positive integers of 1 or greater to block tampering attacks using negative quantities, and apply a pessimistic inventory pre-emption lock to temporarily deduct items with a 15-minute expiration session. Through the process of the server directly reconciling the amount, if even a single won of difference occurs, the transaction is rolled back and a 409 Conflict error is returned, completely preventing financial loss due to hallucinations.
To handle duplicate payments during network timeouts, introduce a distributed idempotency lock engine based on IETF draft specifications. When calling the payment approval API, take an atomic distributed lock in Redis using the idempotency key passed by the agent and perform body fingerprint verification. If the previous request is still being processed, spit out a 409 Conflict; if it's a retry of an already completed request, immediately return the stored response body without hitting the PG approval again. Attaching this idempotency middleware fundamentally prevents duplicate payment accidents caused by network failures and can reduce customer service inquiry counts by more than 80 percent.
If an attacker exploits compromised agent permissions to repeatedly generate small payments or cart creations, PG fees and infrastructure resources will be completely drained. Precise traffic restriction thresholds must be embedded. Using a Redis Sorted Set-based sliding window log algorithm, the backend limits catalog searches to a maximum of 120 times per agent per minute, cart creation to a maximum of 3 active sessions and 20 times per minute, and payment delegation approvals to a maximum of 5 times per agent per minute. Requests exceeding the threshold are immediately defended against resource exhaustion attacks by returning a 429 Too Many Requests error and a retry-after wait time.
To prevent concurrency race conditions, handle per-agent daily transaction limits not in the database, but via Redis Lua scripts. Just before calling the payment approval API, invoke an atomic Lua script running in a single transaction to execute real-time balance deduction reservations. If the limit is exceeded, return 403 Forbidden without even attempting communication with the PG company, and if PG approval fails, return the budget via a compensating transaction. If an abnormal payment failure rate accumulates 3 consecutive times within 1 minute or request volumes exceed limits, a Redis global block key is set, active cart sessions are forcibly canceled, an administrator notification is triggered, and the token is immediately revoked.
