Abstract
In some large-scale data processing environments, such as customer relationship management platforms, automated systems for managing inbound communications can be unreliable. These systems, which can be configured to filter non-actionable cases, may fail silently when platform-enforced processing limits are exceeded, potentially leaving cases unresolved and affecting data integrity. A described technology can provide a framework featuring persistence-first logging and limit-aware dynamic orchestration. For example, before analysis, a durable tracking record can be created to facilitate auditability. An orchestration component can then evaluate platform resource availability and select an execution path, such as synchronous, asynchronous, or batch, to help mitigate failures. This approach can provide a method for managing analysis workloads, for example, by autonomously closing high-confidence non-actionable cases, and may improve process traceability and data quality.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singhal, Parnika and Pauser-Cowman, Martin, "Resilient Automated Communication Management Using Limit-Aware Orchestration and Persistence-First Logging", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12084