Building production AI systems requires more than good prompts — it demands durable execution infrastructure. Using Salesforce's Agentforce Grid as a case study, this post explains how large-scale AI workflows (e.g., generating outputs across 10,000 rows) fail due to distributed systems challenges: partial failures, worker restarts, and unclear retry boundaries. The solution is treating execution as durable state using Temporal-backed workflows, where each row/batch becomes a recoverable unit with its own retry policy and checkpointing. This approach reduced failures from ~90% to 0% in internal testing and improved P95 completion time by ~60%. Key design principles: identify the smallest meaningful unit of recoverable work, persist execution history outside any single process, align retry boundaries with execution boundaries, and expose layered progress visibility to users.
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