Retry Rate and Retry Recovery Rate: Are Retries Fixing Integration Failures?

Retry rate and retry recovery rate provide complementary views of integration health by measuring, respectively, the share of top-level executions that require at least one retry and the share of those retried executions that ultimately succeed within a defined retry policy. Each execution should be counted once regardless of how many attempts it requires, while first-run success should remain a separate measure because eventual success after retries can still add delays or indicate instability. High retry volume paired with strong recovery may reflect effective handling of temporary network or service failures, whereas high retries and weak recovery can indicate persistent issues such as expired credentials, validation failures, or schema mismatches that retries cannot resolve and may merely delay detection. Effective measurement requires clear definitions of executions, reporting windows, automatic versus manual retries, recovery rules, and handling for work still retrying at the end of a period, alongside related metrics such as exhausted retries, execution success, and duration. Results should be segmented by connector, error category, customer, and flow to avoid hiding recurring problems within portfolio averages, while retry policies should also account for potential duplicate processing. Prismatic execution history, GraphQL data, replay information, and observability integrations can help teams identify retried, recovered, and exhausted executions, though teams remain responsible for defining targets and using retries to address transient failures rather than mask persistent problems.

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