What are the underappreciated integration costs when self-building a durable execution layer on Temporal, compared to adopting a managed solution like Catalyst for AI agents?

Self-building on Temporal requires significant effort to retrofit deterministic workflows for AI’s probabilistic nature: designing a custom record-and-replay mechanism for LLM outputs, ensuring idempotency, and tuning the Temporal server for high-cardinality agent task queues. Catalyst provides AI-native replay and a fully managed service. However, Temporal’s mature ecosystem and multi-language support offer flexibility for teams with existing Temporal expertise and diverse workload requirements.