What this error means

batches.retrieve() / batches.results() returns NotFoundError (404) intermittently seconds after successful creation — server-side propagation delay across Anthropic infrastructure nodes is a Anthropic API failure pattern reported for developers trying to handle transient 404 not found errors when calling anthropic batch api retrieve/results endpoints shortly after batch creation. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #1432 on anthropics/anthropic-sdk-python by AlgoApi (Apr 21 2026), detailed case studies. Three failure cases observed: (1) retrieve() returns 404 within 1s of create(), (2) results() returns 404 after batch status confirmed as ended, (3) create() itself returns 404. All eventually succeed — indicates distributed state inconsistency. Strong commercial impact (paid batch API, production-crashing). Category mapping: direct Anthropic API error.

Common causes

  • GitHub Issue #1432 on anthropics/anthropic-sdk-python by AlgoApi (Apr 21 2026), detailed case studies. Three failure cases observed: (1) retrieve() returns 404 within 1s of create(), (2) results() returns 404 after batch status confirmed as ended, (3) create() itself returns 404. All eventually succeed — indicates distributed state inconsistency. Strong commercial impact (paid batch API, production-crashing). Category mapping: direct Anthropic API error.

Quick fixes

  1. Confirm the exact error signature matches batches.retrieve() / batches.results() returns NotFoundError (404) intermittently seconds after successful creation — server-side propagation delay across Anthropic infrastructure nodes.
  2. Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.