Duplicate entries cost you every day.

Retries, ordering and business-level reconciliation

After a timeout, an interface cannot always tell whether the other side has already processed a request. Retrying blindly can then create duplicate bookings. For write operations, we define how a unique identifier maps repeated requests to the original one and how long this information is retained. The identifier alone is not enough: processing and storing the result must fit the transaction model.

With events, we additionally consider ordering and delayed delivery. A cancellation can arrive before a downstream service has processed the original order. Permitted state transitions and version information help handle such cases correctly from a business perspective. Error queues need clear ownership and a controlled restart. A regular reconciliation of important data finds discrepancies that pure monitoring of successful HTTP responses does not detect.

Evolving contracts without surprising connected teams

Besides data types, an API contract also describes meaning, error cases and limits. We clarify whether a missing field, an empty value and an explicit deletion count as different operations. Monetary amounts need a currency and rounding rules, and time values need an unambiguous reference. For large data sets, pagination, filtering options and stable sorting are part of the contract. Example requests make these rules verifiable for consumers.

Even seemingly additive changes can cause problems if a client only accepts known values. That is why we keep track of consumers and check changes against their expectations. New versions get a transition path with documentation, a way to test them and a deprecation plan. For webhooks, we define origin verification and how repeated delivery is handled. A traceable integration history helps support follow a specific business transaction across several systems.

Containing failures and controlling partner access

A slow target system must not create an unlimited number of waiting connections in every upstream service. We plan timeouts, limited retries and capacity limits that fit the business process. Some tasks can be buffered, while others must fail with an understandable response. The fallback value for a failed service must not pretend to be current or approved information.

Access for partners and applications is granted separately and designed to be revocable. Rate limiting alone does not prevent unauthorized data access; business-level permissions are checked in addition. Logs are meant to make errors analyzable without spreading credentials or complete sensitive payload data. The handover therefore also includes credential renewal, alerting on backlogs and a procedure for reprocessing failed messages in a targeted way.