Batch Failure Handling & Constraints

Core

Design efficient batch processing strategies · Difficulty 3/5

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batchfailuresconstraintsoptimization

Batch processing introduces unique failure modes that require specific handling strategies.

Batch Failure Handling

When a batch completes, some requests may fail while others succeed. Handle failures by:

  • Identify failures via custom_id: Each request has a unique custom_id that correlates to the response
  • Resubmit only failed documents: Don't reprocess the entire batch
  • Modify failing requests: Chunk documents that exceeded context limits, simplify prompts that hit edge cases
  • Track failure patterns: If many documents fail for the same reason, fix the root cause before resubmitting
  • Critical Constraint: No Multi-Turn Tool Calling

    The batch API does not support multi-turn tool calling within a single request. Since processing is asynchronous, there is no mechanism to:

  • Intercept a tool call mid-request
  • Execute the tool
  • Return results for Claude to continue
  • This fundamentally breaks iterative tool-calling workflows. Batch is single-turn only.

    Sample-First Strategy

    Before batch-processing large volumes:

  • Run the prompt on a representative sample (50-100 documents)
  • Analyze success rates and failure modes
  • Refine the prompt based on failures
  • Only then submit the full batch
  • This maximizes first-pass success rates and avoids expensive iterative resubmission.

    Key Takeaways

    • Resubmit only failed documents identified by custom_id, not the entire batch
    • Batch API is single-turn only -- no multi-turn tool calling
    • Test prompts on a sample set before batch-processing large volumes
    • Track failure patterns to fix root causes before resubmission